Deep Learning

Deep Learning Engineer Jobs

90 jobs found

Today

AWS AI Engineer

Hudson Manpower Sunnyvale, CA 🇺🇸 Remote

Sunnyvale, CA

Today

Full time AI Artificial Intelligence Automation

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Job Description

The role is for a senior AWS AI Engineer at Hudson Manpower, based in Sunnyvale, California with remote working options. The position centers on developing cloud-native AI/ML applications, with a focus on retrieval-augmented generation, fine-tuning large language models, and deploying AWS-based microservices for an enterprise production environment.

Responsibilities

Hands-on development with AWS services such as Lambda, Bedrock, SageMaker, Step Functions, DynamoDB, and S3 to build and operate AI/ML workloads.
Lead the implementation of AWS cloud services spanning infrastructure, machine learning, and AI platform capabilities.
Work with LLM-based applications, including Retrieval-Augmented Generation (RAG) using LangChain and related frameworks.
Develop cloud-native microservices, APIs, and serverless functions to support intelligent automation and real-time data processing.
Collaborate with internal stakeholders to translate business goals into secure, scalable AI systems.
Own the software release lifecycle, including CI/CD pipelines, GitHub-based SDLC, and infrastructure as code with Terraform.
Support the development and evolution of reusable platform components for AI/ML operations.
Create and maintain technical documentation for the team and internal customers.
Demonstrate strong verbal and written communication skills in English.

Requirements

Minimum of 7 years of hands-on software engineering experience with a strong emphasis on Python.
Experience with AWS services, particularly Bedrock or SageMaker.
Familiarity with fine-tuning large language models and deploying ML models to production, including dataset creation.
Proven experience with AWS Organizations and policy guardrails such as SCP and AWS Config.
Solid background in implementing Retrieval-Augmented Generation (RAG) architectures and LangChain.
Proficiency in Infrastructure as Code practices and building Terraform modules for AWS cloud environments.
Strong background in Git-based version control, code reviews, and DevOps workflows.
History of delivering production-ready software with integrated release pipelines.

Technologies

AWS Bedrock
AWS SageMaker
AWS Lambda
AWS Step Functions
DynamoDB
S3
LangChain
Transformers
PyTorch
TensorFlow
Terraform
Terraform Sentinel
Git
Python
Golang
Node.js
Hugging Face
AWS Organizations
AWS Config
SCP

Benefits

Competitive base salary
Medical, dental, and vision insurance coverage
Optional life and disability insurance
401(k) with company match and optional profit sharing
Paid vacation time
Paid bench time
Training allowance offering
Eligibility for referral bonuses

Supervisory Responsibilities

None

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Today

Machine Learning Engineer - Document Digitization (LLMs)-Vice President

JPMorganChase Jersey City, NJ

Jersey City, NJ

Today

$164k - $260k/yr Full time Ai Ml Amazon Eks Artificial Intelligence

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Job Description

Senior Vice President level Machine Learning Engineer focused on designing, building, and deploying AI powered document digitization solutions using large language models to transform document processing and governance.

Responsibilities

Lead end-to-end design, development, and integration of AI powered document digitization solutions to extract data and insights from diverse document formats.
Manage the full AI/ML lifecycle: training, validation, deployment, monitoring, and ongoing improvements in production environments.
Apply generative AI and LLMs to automate and optimize document workflows.
Build and maintain scalable document digitization pipelines using Python, AI frameworks, and cloud technologies.
Provision and manage cloud resources with infrastructure as code tools (Terraform) and AWS services (SageMaker, Bedrock).
Ensure scalability, reliability, security, and governance compliance of AI/ML solutions.
Collaborate with cross functional teams to reimagine legacy document processing with generative AI and LLMs.
Develop and maintain dashboards and reporting tools to monitor digitization accuracy, workflow efficiency, and business impact.
Mentor junior engineers and promote best practices across AI/ML, software engineering, and testing.
Conduct model validation, human in the loop review, and implement continuous improvement strategies for digitization accuracy.
Contribute to communities of practice and explore new and emerging technologies.

Requirements

Bachelor's or Master's in Computer Science, Data Science, Machine Learning, or related field, with relevant industry experience.
Strong proficiency in Python for building production grade AI services and data/document pipelines.
Strong working proficiency in Java, including API and microservice development with Spring Boot; familiarity with front end technologies (React.js, AngularJS) is a plus.
Hands-on experience delivering LLM powered GenAI applications in production (document understanding, retrieval augmented generation, workflow automation), including evaluation, observability, guardrails, and continuous improvement.
Experience with MLOps / LLMOps practices in production (CI/CD, automated testing, deployment strategies, monitoring, incident response).
Working knowledge of machine learning frameworks (TensorFlow, PyTorch, Scikit-learn, PyTorch Lightning) with emphasis on integrating models into scalable systems.
Experience with AWS and cloud native delivery including SageMaker and/or Bedrock, containerization (Docker, Kubernetes, Amazon EKS), and infrastructure as code (Terraform).
Familiarity with NoSQL / search / graph technologies (MongoDB Atlas, Elasticsearch/OpenSearch, Neo4j) for document search and knowledge retrieval.
Experience with agentic coding approaches, autonomous/assisted code agents, orchestration patterns, and tool-use/agent frameworks to accelerate document digitization workflows.
Strong understanding of SDLC, CI/CD, resiliency, and security practices; proven problem solving, communication, and collaboration skills.
Proven ability to accelerate development using AI technologies while maintaining engineering rigor (testing, code quality, governance).

Technologies

Python
Terraform
AWS SageMaker
AWS Bedrock
Docker
Kubernetes
Amazon EKS
Java
Spring Boot
React.js
AngularJS
TensorFlow
PyTorch
Scikit-learn
PyTorch Lightning
MongoDB Atlas
Elasticsearch/OpenSearch
Neo4j
LLMs

Benefits

Equity/stock-based compensation
Competitive base salary
Commission-based pay and/or discretionary incentive compensation
Comprehensive health care coverage
On-site health and wellness centers
Retirement savings plan
Backup childcare
Tuition reimbursement
Mental health support
Financial coaching

About Us

JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world's most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.

About the Team

Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we're setting our businesses, clients, customers and employees up for success.

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1d ago

Lead Applied AI Engineer II

Deloitte Washington, DC

Washington, DC

1d ago

$119k - $244k/yr Full time Artificial Intelligence AWS Aws Bedrock

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Job Description

Deloitte is seeking a Lead Applied AI Engineer II for an onsite role in Washington, DC. The position centers on hands-on software engineering across high-visibility initiatives, with an emphasis on delivering customer outcomes and tangible business value. The role also invites you to model technical leadership and mentor cross-functional teams to design, build, and deploy advanced solutions. Salary ranges from USD 118,700 to 243,700 per year. A bachelor’s degree and at least six years of relevant experience are required.

Responsibilities

Outcome driven accountability: uphold a culture focused on customer and business outcomes, delivering engineering solutions that solve complex problems with measurable value while maintaining lean, high‑quality designs and implementations.
Technical leadership and advocacy: act as the product’s technical champion, validating code quality and feasibility and aligning work with business and customer goals. lead requirements analysis, low‑level architecture and component design, development, testing, integrations, and support.
Engineering craftsmanship: safeguard architecture integrity and the enterprise standards across the tech stack. manage dependencies, design, implementation, quality, data, and ongoing maintenance. remain hands-on, continuously learn new approaches, and create technical specifications; produce high‑quality, scalable code and review peers’ code while mentoring them to ensure quality.
Customer centric engineering: develop lean solutions through rapid, low‑cost experimentation to address customer needs. engage with customers and product teams before, during, and after delivery to ensure the right solution is delivered at the right time.
Incremental and iterative delivery: adopt a forward‑leaning mindset that emphasizes action and evidence over heavy planning, delivering lean, maintainable solutions that navigate complexity.
Cross-functional collaboration and integration: work with empowered product management, experience, and delivery teams; integrate diverse perspectives to balance feasibility, viability, usability, and value, fostering a collaborative environment that drives innovation.
Advanced technical proficiency: demonstrate deep expertise in modern software engineering practices, including AI and Agentic SSDLC, delivering daily product deployments with full automation from discovery to production to operations and quality checks throughout the SSDLC lifecycle. be a role model and apply these techniques to optimize solutioning and delivery, while focusing on the full lifecycle of product development and continuous improvement.
Domain expertise: rapidly acquire domain knowledge relevant to the business or product; translate business needs, architectures, and UX/UI designs into technical specifications and code; contribute as a flexible, quality‑driven teammate mindful of tech debt payoff.
Effective communication and influence: communicate complex technical concepts clearly and persuasively, inspiring teammates and product teams through well‑structured arguments and evidence‑based trade‑offs; craft narratives that align technical solutions with business objectives.
Engagement and collaborative co‑creation: collaborate with product engineering teams at all levels, including customers when needed; build constructive relationships and foster a culture of co‑creation and shared momentum toward product goals; align diverse perspectives to reach feasible solutions.

Requirements

Bachelor’s degree in computer science, software engineering, data science, machine learning, or a related discipline.
Six or more years of experience with technologies such as Angular, React, NodeJS, Python, C#, .NET, Java, SQL/NoSQL, PyTorch, TensorFlow, LangChain, LangGraph, and relevant unit testing frameworks.
Three or more years building AI/ML applications, with hands-on GenAI experience across LLM integration (OpenAI, Anthropic, or open‑source models), RAG pipelines, prompt engineering, and vector databases.
Three or more years of cloud‑native engineering using FaaS, PaaS, or microservices on Azure, AWS, or GCP, including AI/ML services such as Azure OpenAI, AWS Bedrock, or Vertex AI.
One or more years establishing engineering standards, including actively leading, mentoring, and guiding team members in adopting and improving these standards.
Prior software engineering experience with Business Context Diagrams, sequence/activity/state/entity relationship/data flow diagrams, OOP/OOD, data structures, algorithms, code instrumentation, and AI augmented, spec‑driven development.
Experience using XP, Lean, DevSecOps, SRE, ADO, GitHub, SonarQube, MLflow, and agentic AI frameworks (for example LangFuse, LangSmith, or similar multi‑agent orchestration tools) to deliver high quality products rapidly.

Technologies

Angular, React, NodeJS, Python, C#, .NET, Java
SQL/NoSQL, PyTorch, TensorFlow
LangChain, LangGraph
OpenAI, Anthropic, LangFuse, LangSmith
Azure, AWS, GCP; Azure OpenAI, AWS Bedrock, Vertex AI
FaaS, PaaS, MLflow
ADO, GitHub, SonarQube

The Successful Candidate Will Possess

Excellent interpersonal and organizational skills, with the ability to handle diverse situations, manage complex projects, and adapt to changing priorities with professionalism and empathy.

Other

Travel availability of approximately 10 percent, depending on project requirements.
Limited immigration sponsorship may be available.
The stated wage range reflects a broad range of factors considered in compensation decisions, including skill, experience, and business needs; geographic differentials may apply.
You may be eligible for a discretionary annual incentive program, subject to program rules and individual and organizational performance.

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1d ago

AWS AI Engineer

Hudson Manpower San Jose, CA 🇺🇸 Remote

San Jose, CA

1d ago

Full time AI Ai Workflows Artificial Intelligence

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Job Description

Hudson Manpower is seeking a senior AWS AI Engineer to join their San Jose area team with remote work options. The role centers on hands-on development of AI and ML applications in AWS, with a focus on retrieval-augmented generation (RAG), fine-tuning large language models, and building AWS-native microservices to support enterprise automation and governance.

Responsibilities

Hands-on development across AWS services including Lambda, Bedrock, SageMaker, Step Functions, DynamoDB, and S3.
Own the implementation of AWS cloud services spanning infrastructure, machine learning, and AI platform services.
Work with LLM-based applications, including RAG approaches using LangChain and similar frameworks.
Build cloud-native microservices, APIs, and serverless functions to enable intelligent automation and real-time data processing.
Collaborate with internal stakeholders to translate business goals into secure, scalable AI systems.
Own the software release lifecycle, including CI/CD pipelines, GitHub-based SDLC, and infrastructure as code with Terraform.
Support the development and evolution of reusable platform components for AI/ML operations.
Create and maintain technical documentation for the team and internal customers.
Demonstrate strong English communication skills, both verbal and written.

Requirements

Seven years of hands-on software engineering experience with a strong emphasis on Python.
Experience with AWS services, notably Bedrock or SageMaker.
Familiarity with fine-tuning large language models or building datasets and deploying ML models to production.
Experience with AWS Organizations and policy guardrails such as SCPs and AWS Config.
Proven ability to implement RAG architectures and work with LangChain.
Experience in infrastructure as code best practices and building Terraform modules for AWS.
Solid background in Git-based version control, code reviews, and DevOps workflows.
Proven success delivering production-ready software with integrated release pipelines.

Technologies

Python, Bedrock, SageMaker, Lambda, Step Functions, DynamoDB, S3
LangChain, Transformers, PyTorch, TensorFlow
Terraform, Terraform Sentinel, GitHub
Hugging Face, Node.js, Golang
AWS, SCP, AWS Config

Benefits

Competitive base salary
Medical, dental, and vision insurance coverage
Optional life and disability insurance
401(k) with company match and optional profit sharing
Paid vacation time
Paid bench time
Training allowance
Eligible for referral bonuses

Supervisory Responsibilities

None

Nice-to-Haves

AWS or relevant cloud certifications
Policy as Code development experience (eg, Terraform Sentinel)
Experience with Hugging Face, Golang, or Node.js
Exposure to FinOps and cloud cost optimization
Data science background or experience with structured and unstructured data
Awareness of data privacy and compliance best practices (PII handling, secure model deployment)

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2d ago

AWS AI Engineer

Hudson Manpower Buffalo Grove, IL 🇺🇸 Remote

Buffalo Grove, IL

2d ago

Full time AI Amazon Web Services Artificial Intelligence

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Job Description

The AWS AI Engineer role is a senior position focused on designing and delivering production-grade AI and ML capabilities on AWS. The emphasis is on retrieval-augmented generation, fine-tuning large language models, and building AWS-native microservices to support enterprise automation and governance.

Responsibilities

Hands-on development with AWS services including Lambda, Bedrock, SageMaker, Step Functions, DynamoDB, and S3.
Architect and implement AWS cloud services spanning infrastructure, machine learning, and AI platform capabilities.
Build and deploy LLM-based applications, including Retrieval-Augmented Generation using LangChain and other frameworks.
Develop cloud-native microservices, APIs, and serverless components to enable intelligent automation and real-time data processing.
Collaborate with internal stakeholders to translate business goals into secure, scalable AI systems.
Own the software release lifecycle, including CI/CD pipelines, GitHub-based SDLC, and infrastructure as code with Terraform.
Contribute to reusable platform components for AI/ML operations and tooling.
Create and maintain technical documentation for teams and internal customers.
Maintain strong written and verbal communication in English.

Requirements

Minimum of seven years of hands-on software engineering experience with a strong emphasis on Python.
Proficiency with AWS services, particularly Bedrock, SageMaker, ECS, and Lambda.
Experience with AWS Organizations and policy guardrails, including SCP and AWS Config.
Background in Retrieval-Augmented Generation architectures and using LangChain and other frameworks.
Experience fine-tuning large language models, building datasets, and deploying ML models to production.
Demonstrated IaC expertise, including building Terraform modules for AWS.
Solid Git-based version control practices, code reviews, and DevOps workflows.
Track record of delivering production-ready software with integrated release pipelines.

Technologies

Python
Bedrock
SageMaker
ECS
Lambda
AWS Organizations
SCP
AWS Config
LangChain
Transformers
PyTorch
TensorFlow
Terraform
Terraform Sentinel
AWS Step Functions
DynamoDB
S3
Hugging Face
Node.js
Golang
GitHub

Benefits

Competitive base salary
Medical, dental, and vision insurance coverage
Optional life and disability insurance
401(k) with company match and optional profit sharing
Paid vacation time
Paid bench time
Training allowance
Referral bonuses

Supervisory Responsibilities

None

Minimum Knowledge, Skills, and Abilities Required

Seven years of hands-on software engineering experience with a strong Python focus.
Experience with AWS services, especially Bedrock or SageMaker.
Familiarity with fine-tuning large language models or building datasets and deploying ML models to production.
Experience with AWS Organizations and policy guardrails (SCP, AWS Config).
Solid experience implementing RAG architectures and LangChain.
IaC best practices and Terraform module development for AWS.
Strong Git-based version control, code reviews, and DevOps workflows.
Proven success delivering production-ready software with release pipeline integration.

Nice-to-Haves

AWS or relevant cloud certifications.
Policy as Code development, e.g., Terraform Sentinel.
Experience with Hugging Face, Golang, or Node.js.
Exposure to FinOps and cloud cost optimization.
Data science background or experience with structured/unstructured data.
Awareness of data privacy and compliance best practices, including PII handling and secure model deployment.

What You’ll Get

Competitive base salary
Medical, dental, and vision insurance coverage
Optional life and disability insurance
401(k) with company match and optional profit sharing
Paid vacation time
Paid bench time
Training allowance
Referral bonuses

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2d ago

Lead AI Engineer (Vision model customization, VML)

Capital One McLean, VA

McLean, VA

2d ago

$197k - $225k/yr Full time Ai Ml Artificial Intelligence Cloud

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Job Description

Lead AI Engineer focusing on vision model customization and LLM optimization within Capital One's Intelligent Foundations and Experiences (IFX) team.

Responsibilities

Collaborate with engineers, research scientists, technical program managers, and product managers to deliver AI powered products that transform how associates work and how customers interact with Capital One.
Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability.
Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more.
Invent and apply state-of-the-art LLM optimization techniques to improve scalability, cost, latency, and throughput of large scale production AI systems.
Contribute to the technical vision and long term roadmap of foundational AI systems at Capital One.

Requirements

Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 4 years of AI/ML algorithm or technology development experience, or a Master's degree in the same fields plus at least 2 years of experience.
At least 4 years of experience programming with Python, Go, Scala, or Java.
6 years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g., AWS, Google Cloud, Azure, or equivalent private cloud).
Experience designing, developing, delivering, and supporting AI services.
Experience developing AI and ML algorithms or technologies (e.g., LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, or Golang.
Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost.
Passion for staying abreast of the latest AI research and AI systems, and judiciously applying novel techniques in production.

Technologies

AWS Ultraclusters
Huggingface
VectorDBs
Nemo Guardrails
PyTorch
Python, Go, Scala, Java
C++, C#,
AWS, Google Cloud, Azure

Team overview

The Intelligent Foundations and Experiences (IFX) team centers Capital One's AI initiatives, partnering across the company to advance AI engineering and deploy proprietary solutions that deliver value to millions of customers. Our AI models and platforms empower teams to elevate products with AI at scale.

The ideal candidate

Enjoys building systems, takes pride in high quality work, and aligns with responsible engineering practices to improve banking.
Keeps up with the latest AI research and can digest publications to apply techniques in production.
Adapts quickly toDefine ambiguous problems, asks focused questions, communicates findings clearly, and proposes new ideas when appropriate.
Is deeply technical, with a strong foundation in engineering and mathematics; leverages hardware, software, and AI optimization opportunities.
Acts as a resilient trail blazer who can forge new paths to meet business goals when the route is unclear.

Basic qualifications

Bachelor's degree with 4+ years of AI/ML development experience or a Master's degree with 2+ years of AI/ML development experience.
At least 4 years of experience programming with Python, Go, Scala, or Java.

Preferred qualifications

6 years of experience deploying scalable and responsible AI solutions on cloud platforms (AWS, Google Cloud, Azure, or equivalent private cloud).
Experience designing, developing, delivering, and supporting AI services.
Experience developing AI/ML algorithms or technologies (LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, or Golang.
Experience optimizing training and inference software to improve hardware utilization, latency, throughput, and cost.
Passion for staying current with AI research and AI systems and applying novel techniques in production.

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3d ago

AWS AI Engineer

Hudson Manpower Pittsburgh, PA 🇺🇸 Remote

Pittsburgh, PA

3d ago

Full time AI Amazon Web Services Artificial Intelligence

Apply Now

Job Description

Hudson Manpower is seeking a Senior AWS AI Engineer to architect and operate production AI/ML workloads on AWS, with an emphasis on retrieval-augmented generation, fine-tuning large language models, and cloud-native microservices. The role delivers scalable, secure AI services that integrate with live infrastructure data. Location: Pittsburgh, PA with remote work options.

Responsibilities

Hands-on development and operation of AWS services including Lambda, Bedrock, SageMaker, Step Functions, DynamoDB, and S3.
Design and implement AWS cloud services across infrastructure, machine learning, and AI platform offerings.
Work with LLM based applications, including Retrieval-Augmented Generation (RAG) using LangChain and other frameworks.
Build cloud-native microservices, APIs, and serverless functions to support intelligent automation and real-time data processing.
Collaborate with internal stakeholders to translate business goals into secure, scalable AI solutions.
Own the software release lifecycle, including CI/CD pipelines, GitHub-based SDLC, and infrastructure as code with Terraform.
Contribute to the development and evolution of reusable platform components for AI/ML operations.
Create and maintain technical documentation for the team and internal customers.

Requirements

Seven years of hands-on software engineering experience with a strong focus on Python.
Experience with AWS services, particularly Bedrock or SageMaker.
Familiarity with fine-tuning large language models or building datasets and deploying ML models to production.
Experience with AWS Organizations and policy guardrails such as SCP and AWS Config.
Solid background implementing RAG architectures and using LangChain.
Practices in Infrastructure as Code and building Terraform modules for AWS cloud.
Strong Git-based version control, code reviews, and DevOps workflows.
Proven success delivering production-ready software with release pipeline integration.

Technologies

Python
Bedrock
SageMaker
ECS
Lambda
Step Functions
DynamoDB
S3
LangChain
Transformers
PyTorch
TensorFlow
Terraform
Git
GitHub
Hugging Face
Node.js
Golang
Terraform Sentinel
AWS Config
SCP

Benefits

Competitive base salary
Medical, dental, and vision insurance coverage
Optional life and disability insurance provided
401(k) with a company match and optional profit sharing
Paid vacation time
Paid Bench time
Training allowance offering
Referral bonuses

Top Skills

AWS services including Bedrock, SageMaker, ECS, and Lambda
Experience with AWS Organizations and policy guardrails (SCP, AWS Config)
Proficiency in implementing RAG architectures and using ML tooling such as Transformers, PyTorch, TensorFlow, and LangChain
Infrastructure as Code practices and building Terraform modules for AWS
Fine-tuning large language models, building datasets, and deploying ML models to production
Git-based version control, code reviews, and DevOps workflows
AWS or other cloud certifications
Data privacy and compliance best practices (PII handling, secure model deployment)
Background in data science or experience with structured and unstructured data
Exposure to FinOps and cloud cost optimization
Hugging Face and Node.js experience
Policy as Code development (Terraform Sentinel)