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Cloud Engineer Jobs

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4d ago

Machine Learning Engineer 5, Senior Manager - Intelligent Foundations and Experiences (IFX)

Information Technology Senior Management Forum McLean, VA

McLean, VA

4d ago

$230k - $262k/yr Full time Backend Developer Manager Ai Ml

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Job Description

This role is for a Senior Manager focused on building and scaling AI and machine learning capabilities, including model training and serving in production and multi-tenant ML platforms. You will work with Product and Data Science teams to deliver reliable data pipelines, CI/CD workflows, and Responsible and Explainable AI practices.

Location

McLean, VA (onsite)

Salary

McLean, VA: $229,900 - $262,400 per year (Machine Learning Engineer 5)
Richmond, VA: $209,000 - $238,500 per year (Machine Learning Engineer 5)

Role Summary

You will design and deliver ML models and platform components that address business needs, while using your depth in modeling and ML infrastructure decisions. The work includes training, deploying, and maintaining models in production, scaling large footprint training and serving, and ensuring governance and security best practices for risk and Responsible and Explainable AI.

Responsibilities

Design, build, and/or deliver ML models and components to solve real-world business problems in collaboration with Product and Data Science teams
Build and scale multi-tenant platforms enabling large footprint ML training and/or serving at scale
Use ML modeling fundamentals to guide infrastructure decisions, including model choice, data and feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation
Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
Collaborate in a cross-functional Agile team to create and enhance software for big data and ML applications
Retrain, maintain, and monitor models in production
Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale
Construct optimized data pipelines to feed ML models
Apply CI/CD best practices, including test automation and monitoring, to support successful model and application deployments
Ensure code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and ML follows Responsible and Explainable AI best practices
Use programming languages such as Python, Scala, or Java

Required Qualifications

Bachelor's Degree or higher in Computer Science, Machine Learning, or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
At least 6 years of experience programming with Python, Java, Golang, or C++
At least 6 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries Pandas, NumPy, Scikit-learn
At least 6 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data
At least 4 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized Machine Learning systems

Technologies

Python, Scala, Java, Golang, C++
PyTorch, Tensorflow, Pandas, NumPy, Scikit-learn
Spark, Ray
AWS, GCP, Azure
Kubernetes, CI/CD

Benefits

Eligible for performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)
Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits supporting total well-being

Preferred Qualifications

Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field
+2 years experience building production RAG pipelines (chunking, embeddings, vector indexing, graph RAG, or fine-tuning)
+2 years experience leveraging Agentic patterns (Tool Calling, ReAct loops, Plan-and-Execute, Multi-Agent Collaboration)
+2 years with LLM orchestration frameworks (LangChain, LangGraph, LlamaIndex, Semantic Kernel) and model APIs (OpenAI, Anthropic, Bedrock, Ollama, Hugging Face)
+2 year experience in building agentic AI systems and agentic workflows
+2 year experience developing AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, CUDA, or Golang
+2 years experience right-sizing models, instance counts, and hardware types given requirements (e.g., context length, token inputs, token outputs)
5+ years of experience optimizing ML algorithms, configurations, and infrastructure
5+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc.
5+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans
5+ years of experience working with Machine Learning techniques and model types, including evaluation and diagnosing issues such as underfitting and overfitting
5+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models
ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents
Ability to communicate complex technical and machine learning concepts clearly to a variety of audiences

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4d ago

Machine Learning Engineer

Adidev Technologies Inc Seattle, WA

Seattle, WA

4d ago

Full time Ai Ml Artificial Intelligence Automation

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Job Description

Adidev Technologies Inc is hiring a Machine Learning Engineer in Seattle, WA (onsite) to deploy advanced ML solutions with a focus on Generative AI.

Responsibilities

Design, refine, and iterate on advanced machine learning models and algorithms, turning complex datasets into actionable outcomes.
Own the end-to-end lifecycle of data modeling projects, from clarifying business requirements through deployment and monitoring.
Perform comprehensive data analysis, including preprocessing, feature engineering, and applying Generative AI methods for novel solution development.
Drive cross-functional collaboration to integrate Generative AI models into product offerings, improving capabilities and user experience.
Use advanced analytics to examine large datasets, surfacing trends, anomalies, and optimization opportunities.
Improve model accuracy and efficiency via data preprocessing, feature engineering, and algorithm optimization.
Conduct exploratory data analysis to extract insights that inform strategic decisions.
Stay current with ML trends, tools, and best practices, including AutoML, MLOps, and interpretability frameworks.
Support ethical AI adoption while meeting compliance expectations and applicable industry standards and regulatory requirements.

Requirements

Degree in Computer Science, Engineering, Statistics, or a related technical field.
Demonstrable experience in machine learning and one or more: deep learning, NLP, computer vision, reinforcement learning, or other AI domains.
Demonstrable experience with Generative AI models and frameworks, including industry applications of GANs or Transformers.
Practical experience with SQL/NoSQL databases, data visualization tools, and version control systems.
Strong foundation in algorithmic complexity and data structure optimization.
Excellent problem-solving, collaboration, and communication skills.
Ability to develop and deploy cutting-edge machine learning models, especially Generative AI use cases such as text generation, image synthesis, and creative AI.
Experience executing end-to-end data analysis work: preprocessing, feature engineering, and leveraging Generative AI algorithms for new solutions.
Stay ahead of AI research, particularly Generative AI, applying new findings and techniques to project work.
Proficiency in Python and ML libraries, including TensorFlow, PyTorch, and scikit-learn.
Strong background in cloud computing and big data platforms (AWS, Azure, GCP), including hands-on use of cloud ML services and serverless architectures.
Familiarity with DevOps for AI: containerization (Docker, Kubernetes), CI/CD pipelines, and MLOps practices.
Capability to facilitate knowledge sharing and best practices across AI/ML, with emphasis on Generative AI.
Commitment to ethical guidelines and data privacy standards for all AI implementations.

Technologies

Python
TensorFlow
PyTorch
scikit-learn
GANs
Transformers
SQL
NoSQL
AWS
Azure
GCP
Docker
Kubernetes

Benefits

Competitive salary range based on experience and market value.
Paid relocation.
Support, including remote assistance.
Regular salary reviews.

How we’ll guide you

Teaching and development to support growth into an exceptional consultant delivering top-tier solutions to end clients.
Custom support from multiple teams, including Development Managers and Tech Subject Matter Experts.
Project placement support through a market-expertise team to help secure and thrive in client projects.
Career growth support to build industry experience and advance your technical journey.

How to apply

Submit a comprehensive application, including a latest updated resume and a detailed cover letter.

Equal Opportunity: Adidev Technologies Inc. welcomes applications from candidates of all backgrounds, experiences, and walks of life.

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4d ago

Sr. Machine Learning Engineer

Tesla Fremont, CA

Fremont, CA

4d ago

$124k - $258k/yr Full time Backend Developer Senior Artificial Intelligence

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Job Description

Tesla is hiring a Sr. Machine Learning Engineer in Fremont, CA with onsite work. This role focuses on designing, building, and productionizing backend machine learning services that support Tesla Construction organization applications. You will own the technical path from problem definition through deployment, connecting data pipelines, model training and serving, and dependable production monitoring.

Benefits

Medical plans with plan options and $0 payroll deduction
Family-building benefits including fertility, adoption, and surrogacy
Dental (including orthodontic coverage) and vision plans, both with options and $0 paycheck contribution
Company paid HSA contribution when enrolled in the High-Deductible medical plan with HSA
Healthcare and Dependent Care Flexible Spending Accounts (FSA)
401(k) with employer match, Employee Stock Purchase Plans, and other financial benefits
Company paid Basic Life and AD&D
Short-term and long-term disability insurance (90 day waiting period)
Employee Assistance Program
Sick and Vacation time (Flex time for salary positions, accrued hours for hourly positions) and Paid Holidays
Back-up childcare and parenting support resources
Voluntary benefits including: critical illness, hospital indemnity, accident insurance, theft & legal services, and pet insurance
Weight Loss and Tobacco Cessation Programs
Tesla Babies program
Commuter benefits, plus employee discounts and perks
Earned Wage Access: real-time pay access paid daily, subject to eligibility and schedule (applicable for below Sr. Managers levels)

Responsibilities

Design and build backend ML services and APIs supporting forecasting, classification, anomaly detection, and decision-support use cases
Own the end-to-end path from data to production, including feature pipelines, model training, evaluation, deployment, and monitoring
Partner with product and business stakeholders to define problems, establish success metrics, and connect model outputs to value within existing workflows
Integrate models into scalable backend systems with clear contracts, versioning, and failure handling
Increase production reliability, latency, and data quality using logging, drift monitoring, and retraining strategies
Review data, feature work, and model design with the team and provide constructive technical feedback
Mentor engineers on ML engineering practices, testing, and production standards
Share knowledge across software, data, and operations teams, and communicate trade-offs in plain language

Requirements

Bachelor’s Degree in Computer Science, Machine Learning, or related field, or equivalent experience
5+ years building production backend systems, including 3+ years shipping machine learning models or ML services
Strong proficiency in Python for data processing, model development, and service development
Experience with ML frameworks such as scikit-learn, PyTorch, or similar, including model evaluation and experiment tracking
Experience designing and serving models behind APIs, including REST service design, versioning, and integration patterns
Strong SQL skills, including experience with relational databases such as SQL Server, and feature/data pipeline design
Experience productionizing models with Docker, CI/CD, and cloud platforms (Azure preferred)
Working knowledge of performance, reliability, logging, monitoring, and scalability for production ML systems
Experience writing unit and integration tests for data pipelines, model logic, and services
Experience mentoring engineers and driving technical decisions from problem framing through deployment

Expected Compensation

$124,000 - $258,000 per year salary + cash and stock awards + benefits.

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5d ago

Senior Data Engineer

Robotics Technologies Atlanta, GA

Atlanta, GA

5d ago

Full time Senior Automation Big Data

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Job Description

Senior Data Engineer role at Robotics Technologies in Atlanta, GA (onsite), focused on building and operating production batch and streaming data pipelines and lakehouse integrations.

Responsibilities

Build and run production-grade batch and streaming data pipelines across SQL Server, cloud applications, device and event streams, Customer Relationship Management systems, and third-party sources.
Design ingestion, transformation, and enrichment workflows using Apache Flink, Kafka, Change Data Capture, SQL, and Python/Java/Scala.
Implement lakehouse layers across landing, raw, conformed, and consumption zones using Apache Iceberg and Trino.
Develop dimensional and analytical models for entities including buildings, tenants, credentials, devices, and people.
Handle CDC challenges including late-arriving data, retries, backfills, replay, schema evolution, and data reconciliation.
Build and maintain lakehouse to Snowflake integrations, including Iceberg-backed/external tables, secure sharing, and downstream data contracts.
Automate data-quality checks, pipeline monitoring, lineage, freshness alerts, and operational dashboards.
Troubleshoot and resolve production data issues, pipeline failures, performance bottlenecks, and data-quality problems.
Optimize SQL and platform efficiency: storage layouts, partitioning, clustering, compute utilization, and overall platform cost.
Implement security controls including role-based access, row/column-level security, masking, and protections for sensitive data.
Deliver and support CI/CD with automated testing, infrastructure-as-code, and environment promotion processes.
Participate in production support, incident resolution, Root Cause Analysis, deployments, and on-call activities.
Collaborate with Product, Application Engineering, Quality Assurance, DevOps/Site Reliability Engineering, Analytics, and globally distributed engineering teams.
Contribute to technical design decisions and documentation while staying hands-on with implementation and production delivery.

Requirements

6+ years of hands-on experience in data engineering, data warehousing, or data platform development.
Strong production experience building and supporting end-to-end data pipelines.
Expert SQL with complex query writing, query-plan analysis, and performance tuning.
Strong development skills in Python, Java, or Scala.
Hands-on experience with Apache Iceberg, Delta Lake, or Apache Hudi.
Experience with distributed query technologies such as Trino, Presto, or Spark SQL.
Hands-on Snowflake experience including data modeling, performance optimization, external/Iceberg tables, secure data sharing, or access management.
Experience with streaming and Change Data Capture technologies such as Apache Flink, Kafka, Spark Structured Streaming, or Debezium.
Strong understanding of dimensional modeling, data quality, schema evolution, and data contracts.
Experience with Kubernetes, containers, CI/CD, and infrastructure-as-code.
Experience implementing monitoring, alerting, lineage, reconciliation, and operational support for production data platforms.
Ability to independently troubleshoot complex production issues and drive them through resolution.

Technologies

SQL Server, Apache Flink, Kafka, Change Data Capture, SQL
Python, Java, Scala
Apache Iceberg, Trino, Snowflake, Apache Hudi, Delta Lake
Presto, Spark SQL, Spark Structured Streaming, Debezium
Kubernetes, Continuous Integration, Continuous Deployment, Infrastructure-as-code
Row/column-level security

Preferred Qualifications

Experience with multi-tenant Software as a Service data platforms and customer data isolation.
Experience integrating open lakehouse platforms with Snowflake.
Azure cloud experience.
Experience with Airflow, Dagster, dbt, Power BI, Looker, or Superset.
Experience with data governance, cataloging, masking, and access-control frameworks.
Exposure to Internet of Things/device telemetry, physical security, commercial real estate, or property technology.
Experience using Artificial Intelligence-assisted engineering tools to accelerate data development.

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5d ago

Data Engineer

Robotics Technologies Plano, TX

Plano, TX

5d ago

Full time Backend Developer Python Amazon Web Services

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Job Description

Robotics Technologies is hiring a hands-on Data Engineer in Plano, TX, onsite. This role focuses on building and maintaining reliable data integrations and pipeline workflows using Python and AWS Glue, with support for enterprise data platform connectivity and data lake or lakehouse environments.

Role Responsibilities

Design, build, and maintain data pipelines using Python and AWS services.
Develop data ingestion and connectivity solutions for enterprise data platforms.
Work with AWS data lake services including Amazon S3, AWS Glue, and Lake Formation.
Use AWS CLI and EC2 to support engineering and data workloads.
Implement and maintain CI/CD pipelines using Jenkins and Bitbucket.
Troubleshoot pipeline failures and improve data quality and reliability.
Collaborate with platform and engineering teams to support shared data services.

Required Qualifications

Strong hands-on Python programming experience.
Practical AWS experience including S3, AWS CLI, EC2, AWS Glue, and Lake Formation.
Understanding of data lakes and data pipeline development.
Experience with CI/CD pipelines, including Jenkins and Bitbucket.
Ability to build, test, troubleshoot, and maintain production data pipelines.

Key Technologies

Experience with the following technologies is relevant to this role: Python, AWS, AWS Glue, Amazon S3, Lake Formation, AWS CLI, EC2, Jenkins, Bitbucket, Snowflake, Databricks, Apache Iceberg, AWS Bedrock, Terraform, PySpark, and Apache Spark.

Preferred Skills

Familiarity with Snowflake, Databricks, and Apache Iceberg.
Experience with AWS Bedrock and exposure to AI agentic workflows.
Experience with Terraform, PySpark, or Apache Spark.

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5d ago

Data Engineer

Robotics Technologies San Jose, CA

San Jose, CA

5d ago

Full time Analytics Automation Developer AWS

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Job Description

Robotics Technologies is hiring a Data Engineer in San Jose, CA (onsite) to build scalable pipelines and data processing solutions across multiple sources and cloud platforms.

Responsibilities

Design, develop, and maintain scalable data pipelines for extract, transform, and load workflows across multiple sources.
Build ETL/ELT workflows using Python, SQL, PySpark, and Azure Data Factory.
Develop and optimize data processing solutions with Apache Spark and Databricks.
Work with structured and unstructured data from relational databases, REST APIs, flat files, and cloud storage.
Implement data integration solutions that support business intelligence and analytics needs.
Write complex SQL for large datasets, including stored procedures, views, and functions.
Create data validation and cleansing routines, including data transformation and data quality checks to improve accuracy.
Store and manage large data volumes using Azure Data Lake Storage and related cloud data platforms.
Develop and maintain data warehouses, data marts, and dimensional data models.
Integrate data from enterprise systems using REST APIs and database connectors.
Monitor pipeline health, diagnose failures, and resolve performance and processing issues.
Implement incremental loading, partitioning, and optimization techniques to improve pipeline performance.
Use Snowflake for loading, transformation, query optimization, and warehouse management.
Apply data security, access controls, and governance practices to protect sensitive information.
Use Git and Azure DevOps to manage source control plus CI/CD and deployment activities.
Collaborate with data analysts, data scientists, and business stakeholders to gather and refine data requirements.
Participate in Agile/Scrum ceremonies, technical discussions, code reviews, and production support.

Requirements

Bachelor's degree in Computer Science, Information Technology, or a related field.
Experience in data engineering, ETL development, and data pipeline implementation.
Strong programming knowledge in Python, SQL, and PySpark.
Experience with cloud platforms and data warehousing technologies.
Understanding of data modeling, database concepts, and data integration.
Strong analytical, problem-solving, debugging, and communication skills.

Skills & Technologies

Programming: Python, SQL, PySpark
Big Data: Apache Spark, Hadoop, Databricks
ETL Tools: Azure Data Factory, Informatica, SSIS
Cloud Platforms: Microsoft Azure, AWS
Data Storage: Azure Data Lake, Amazon S3
Data Warehousing: Snowflake, Azure Synapse Analytics, SQL Server
Databases: PostgreSQL, MySQL, Oracle, SQL Server
Data Modeling: Star Schema, Snowflake Schema, Dimensional Modeling
DevOps: Git, Azure DevOps, CI/CD, Jenkins
Reporting: Power BI
Methodologies: Agile, Scrum, SDLC
APIs & Connectors: REST APIs
Other Tools/Platforms: Azure Synapse Analytics, Azure Data Lake Storage, Hadoop, Informatica, SSIS

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5d ago

Data Engineer 5

Capital One New York, NY

New York, NY

5d ago

$251k - $286k/yr Full time Backend Developer Apache Airflow Automation

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Job Description

Capital One seeks a Data Engineer 5 (Senior Manager, IC) in Enterprise Data Tech (EDT) to lead a major data transformation. The role focuses on designing and delivering scalable, resilient, cloud-first data pipelines and platforms, leveraging technologies aligned with modern AI workloads.

Key Responsibilities

Partner with and across Agile teams to design, develop, test, implement, and support technical solutions using full-stack development tools and technologies
Provide technical influence to a team of developers, data analysts, and data scientists with expertise in machine learning, distributed microservices, lakehouse architecture, and full-stack systems
Build solutions using Python and Spark, integrating open-source relational and NoSQL databases with cloud data warehousing platforms such as Databricks and Snowflake
Stay current with data and platform trends by experimenting with and learning new technologies, contributing to internal and external technology communities, and mentoring other members of the data community
Collaborate with product managers and software engineers to deliver robust cloud-first data solutions that support experiences used by millions of Americans
Independently design, build, and deliver cloud data solutions and applications with minimal guidance
Architect and enforce common data engineering design patterns to improve code quality, maintainability, and reusability across platforms and pipelines
Design and build data pipelines and platforms with a focus on scalability, resilience, and operational efficiency, including performance under increasing data volume and business demand
Act as a force-multiplier by combining hands-on engineering contributions and innovation with mentoring and skills elevation for peers and junior engineers
Lead end-to-end, large-scale data transformation initiatives, including critical architectural decisions and platform evaluation (for example, Snowflake versus Databricks) based on technical and business requirements
Serve as an ambassador for the data engineering team by clearly communicating technical concepts and data outcomes to internal and external stakeholders

Required Qualifications

Bachelor’s degree or higher in Computer Science or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
At least 6 years of experience in application development (internship experience does not apply)
At least 4 years of experience in distributed data
At least 4 years of experience with SQL
At least 4 years of programming experience with at least one of the following: Python, Java, or Scala
At least 4 years of experience designing and developing data pipelines
At least 2 years of experience in data modeling and designing end-to-end data solutions using both relational and non-relational database systems

Technologies and Tools

Python, Spark, Databricks, Snowflake, SQL, NoSQL, relational databases, AWS, EMR, Glue, lakehouse architecture, microservices, Redshift, MongoDB, Cassandra, DynamoDB, Airflow, Dagster, Monte Carlo, Splunk, real-time, streaming data pipelines

Preferred Qualifications

Master’s degree in Computer Science or a related field
8+ years of experience in data engineering
5+ years of hands-on experience designing, deploying, and operating data workloads in AWS
5+ years of experience building or supporting distributed data or compute workloads using tools such as EMR, Spark, Glue, or Databricks
5+ years of experience designing, implementing, and operating real-time or streaming data pipelines
5+ years of experience working with unstructured or semistructured data using NoSQL databases (for example: MongoDB, Cassandra, DynamoDB)
5+ years of experience designing, implementing, and supporting data warehousing solutions (for example: Snowflake, Redshift)
4+ years of data modeling experience
3+ years of experience working in an Agile development environment
3+ years of experience developing user-centric reusable data products
3+ years of experience working on data observability (for example: Monte Carlo, Splunk) or data orchestration tools (for example: Airflow, Dagster)

Location and Compensation

Location: New York, NY (onsite)

Estimated Salary: USD 250,800 - 286,200 per year

Benefits

Health, financial, and other benefits supporting your total well-being
Performance based incentive compensation, including cash bonus(es) and/or long term incentives (LTI)