Rag Architectures

Rag Architectures Engineer Jobs

81 jobs found

Today

AI Engineer – Software Development Tools

Hewlett Packard Enterprise San Juan, PR

San Juan, PR

Today

Full time Agentic Ai Ai Agent Ai Agent Platform

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Job Description

Hybrid role in San Juan, PR focused on building AI-powered capabilities that enhance software development workflows using Generative AI.

Responsibilities

Develop and integrate AI and Generative AI features into software development tools across coding, code understanding, code review, testing, debugging, build, CI/CD, and release workflows.
Build LLM-based applications using RAG, AI agents, tool calling, MCP, embeddings, and vector databases.
Create AI assistants and automation to support engineers with code comprehension, failure diagnosis, test generation, defect analysis, and issue resolution.
Integrate AI capabilities with existing engineering systems, including APIs, source-code repositories, CI/CD pipelines, issue tracking, build systems, and developer environments.
Design and implement agentic workflows that can reason over engineering data and take actions through approved tools and APIs.
Evaluate models, prompts, agents, and architectures for accuracy, latency, cost, reliability, and developer value.
Build AI quality evaluation mechanisms using automated evaluation, human feedback, regression testing, and monitoring of AI-generated outputs.
Partner with software development teams to identify pain points and translate them into practical AI-powered solutions.
Develop scalable, secure, maintainable AI services for enterprise engineering organizations.
Instrument AI applications to measure adoption, productivity impact, quality improvements, and business value.
Contribute to design reviews, code reviews, architecture discussions, and engineering best-practice initiatives.
Stay current with evolving AI technologies and identify opportunities to apply relevant advancements to internal developer tooling.

Requirements

Bachelor’s or Master’s degree in Computer Science, Software Engineering, AI/ML, or a related technical field.
2–6 years of software engineering or AI/ML engineering experience, including hands-on work building production-quality software.
Practical experience building applications with Generative AI and Large Language Models (LLMs).
Strong programming skills in Python and/or JavaScript/TypeScript with solid software engineering fundamentals.
Experience with LLM APIs, prompt engineering, structured outputs, embeddings, RAG, or agent-based applications.
Experience developing and consuming REST APIs and microservices.
Strong understanding of the software development lifecycle, including source control, CI/CD, testing, debugging, and engineering workflows.
Familiarity with cloud platforms, containers, Kubernetes, or modern deployment practices.
Ability to collaborate with software developers and translate engineering problems into technical solutions.
Strong analytical, problem-solving, and communication skills.

Technologies

Generative AI, LLMs, agents, RAG, intelligent automation
LLM APIs, prompt engineering, structured outputs, tool calling
MCP, embeddings, embeddings-based systems, vector databases
REST APIs, microservices, source-code repositories, issue tracking, build systems
CI/CD, Python, JavaScript/TypeScript
Cloud platforms, containers, Kubernetes

What You Will Build

AI-powered code understanding and developer assistants
Intelligent code review and code-quality analysis
Automated unit-test and test-case generation
AI-based build and CI/CD failure diagnosis
Intelligent debugging and root-cause analysis
AI-powered software defect and issue analysis
Developer-facing RAG/knowledge assistants
Agentic automation for repetitive software engineering tasks
AI-driven engineering insights and productivity tools
Intelligent automation across the software development lifecycle

Impact

Apply AI at scale to the daily workflows of software engineers.
Reduce repetitive engineering work, accelerate development and debugging, and improve software quality.
Enable developers to spend more time on higher-value engineering activities.

Benefits

Relocation support provided to eligible candidates
Health & Wellbeing benefits

Accessibility

HPE is committed to creating an inclusive and accessible workplace and encourages applications from all qualified individuals, including those with disabilities.
Accommodation request process available for candidates needing assistance during the application or interview process.

Additional Information

Health & Wellbeing
Personal & Professional Development
Unconditional Inclusion
Let’s Stay Connected

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1d ago

AI & Data Engineer

EVERFORCE LLC Santa Clara, CA

Santa Clara, CA

1d ago

$110k - $150k/yr Full time Ai Engineering Ai Ml Api Integration

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Job Description

The AI & Data Engineer at EVERFORCE LLC will design, build, and maintain scalable data pipelines and ETL/ELT processes, and will develop and operate AI/ML models in production environments. The role also supports integration with enterprise systems and cloud platforms, along with documentation and governance-focused deliverables.

Responsibilities

Design, build, and maintain scalable data pipelines and ETL/ELT workflows to ingest, clean, transform, and integrate structured and unstructured data from enterprise systems and other data sources.
Develop, train, test, and deploy machine learning and artificial intelligence models, including integration of large language models (LLMs) where applicable. Support prompt engineering and retrieval-augmented generation (RAG) pipelines as needed.
Integrate AI/ML models and data pipelines with enterprise applications, APIs, and cloud-based platforms.
Monitor, troubleshoot, and optimize performance, quality, and reliability of data pipelines and models. Include data validation and resolution of data or model issues.
Maintain documentation, metadata, and data lineage to support governance, transparency, and auditability.
Collaborate with IT, data science, and business teams to gather requirements, refine solutions, and support ongoing AI and data initiatives.

Technologies

ETL/ELT
Machine learning
Artificial intelligence
Large language models (LLMs)
Prompt engineering
Retrieval-augmented generation (RAG)
APIs
Cloud-based platforms

Deliverables

Documented, production-ready data pipelines and ETL/ELT jobs, including source-to-target mapping and data quality checks.
Trained, validated, and deployed AI/ML models (or model enhancements), along with relevant performance and evaluation metrics.
Integrations connecting AI/ML models and data pipelines to enterprise systems, applications, and/or cloud platforms.
Dashboards, reports, or monitoring tools to track data quality, pipeline health, and/or model performance.
Technical documentation covering data pipeline architecture, data lineage, model design, and deployment processes.
Periodic status updates and knowledge-transfer materials for IT and business teams.

Location and Compensation

Location: Santa Clara, CA (onsite)

Salary: USD 110,000 - 150,000 per year

Experience

Minimum experience: 2 years

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2d ago

Data Scientist II - ML Engineering

HEB San Antonio, TX

San Antonio, TX

2d ago

Full time Artificial Intelligence CI/CD Data Pipeline

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Job Description

Lead ML engineering across an enterprise ML platform, production model lifecycle, and Generative AI workflows.

Responsibilities

Design, create, and maintain an ML platform and related environments
Manage Docker containers and Kubernetes clusters, including dependencies and configurations
Implement CI/CD pipelines to automate building, testing, and deployment of machine learning models
Monitor and optimize model training performance and resource usage
Deploy machine learning models to production environments
Manage model versioning and rollback mechanisms
Enable scalable, reliable model serving using tools such as Vertex, Databricks, TensorFlow Serving, Flask, or FastAPI
Work with data scientists, data engineers, and stakeholders to understand and fulfill infrastructure needs
Stay current on latest ML infrastructure technologies and best practices
Architect and develop Generative AI solutions using Machine Learning and GenAI techniques
Engineer and deploy Generative AI models focused on Retrieval-Augmented Generation (RAG), search, knowledge graphs, and multi-agent workflows
Prepare both unstructured and structured data for Language Model Learning (LLM) context, including:
- Embedding large text corpora
- Developing generative SQL queries
- Building connectors to structured databases
Train models on prepared data and tune fine-tuning hyperparameters for optimal performance

Analytics / Design & Development

Build a framework to stitch cross-domain learning and optimize for mission-specific and multi-mission tasks
Specialize in AI interpretation and causality:
- Uncover model causality relationships
- Build a framework to measure bias, underspecification, and latent drivers with their connections
Create an enterprise domain-specific reasoning system to improve actionable insights and optimize machine learning resource usage
Orchestrate reusable storytelling methodology to support AI translation
Apply inquisitive, transparency-focused approaches for ML/AI visibility to the business
Use AI reasoning to produce business action recommendations
Apply AI research to accelerate business innovation

Requirements

Related degree or comparable formal training, certification, or work experience
5+ years of experience in a retail or retail-related decision science role
Expertise in ML visualization flow
Expertise in optimizing distributed machine learning in a heterogeneous domain environment
Programming knowledge: SQL, R, Python, Scala, Java, C/C++
Big data / ML optimization knowledge, including:
- GPU code optimization
- Horovod
- Spark MLlib optimization
- Cython, JNI, Numba
Mainstream ML / AI knowledge, including:
- Manifold learning
- Distributed clustering
- Graph network
- Hierarchical model
- Bayesian network
- Deep learning
- Computer vision
- NLP/NLU
- Reinforcement learning
- Meta-learning
- Federated learning
Skills to apply causal reasoning representation and learning, plus human-centric, explainable, responsible AI
Ability to act as a creative storyteller and translator between business questions and ML solutions
Ability to work with imperfect or incomplete data
Ability to apply AI reasoning into business action recommendation
Comfort working in a fast-paced retail environment with frequently shifting priorities
Ability to work extended hours and sit for long periods

Technologies

Docker, Kubernetes, CI/CD
Vertex, Databricks, TensorFlow Serving, Flask, FastAPI
Machine Learning, GenAI, Retrieval-Augmented Generation (RAG), Language Model Learning (LLM)
SQL, R, Python, Scala, Java, C/C++
GPU code optimization, Horovod, Spark MLlib, Cython, JNI, Numba
Manifold learning, distributed clustering, graph network, hierarchical model, Bayesian network
Deep learning, computer vision, NLP/NLU, reinforcement learning, meta-learning, federated learning

Location: San Antonio, TX (onsite)

Experience: 5+ years

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2d ago

PLM AI Engineer

Wipro Limited Sunnyvale, CA

Sunnyvale, CA

2d ago

$60k - $140k/yr Full time Backend Developer Python Agentic Ai

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Job Description

Build AI that helps resolve real-world support questions. This role at Wipro Limited in Sunnyvale, California (onsite) focuses on designing and delivering an AI-powered support bot and agentic workflows for Hypercare query resolution. You will create backend services in Python, implement RAG and multi-source retrieval for better accuracy, and work closely with Hypercare, PLM, and support teams to continuously improve responses. The position also emphasizes Apple security, governance, and AI platform compliance as you develop and deploy solutions.

Responsibilities

Design and build an AI-powered support bot (web/chat-based) to support Hypercare query resolution.
Develop agentic workflows that retrieve and synthesize answers from multiple sources, including:
- Past incident tickets
- Knowledge bases and documentation
- PLM systems (Teamcenter)
Implement RAG / multi-source retrieval pipelines to produce contextual, accurate responses.
Build APIs and backend services using Python and FastAPI, leveraging Apple-approved AI frameworks.
Ensure compliance with Apple security and governance and AI platform constraints throughout development and integration.
Collaborate with Hypercare, PLM, and support teams to improve response accuracy over time.

Requirements

Mandatory skills: AI for Product Software Engineering.
Experience: 3-5 years.

Technologies

Python
FastAPI
RAG / multi-source retrieval
Apple-approved AI frameworks
Teamcenter
Web/chat-based experiences

Benefits

Full range of medical and dental benefits options
Disability insurance
Paid time off including sick leave
Other paid and unpaid leave options

Compensation: USD 60,000 - 140,000 per year.

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2d ago

Staff AI Engineer/AI Solution Architect, Conversation Intelligence Systems

Xenoss New York, NY

New York, NY

2d ago

Full time Backend Developer Ai Solutions Architecture Analytics

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Job Description

Xenoss is hiring a Staff AI Engineer / AI Solution Architect to lead the applied AI architecture for a long-term In-Call Assistant initiative for a world-leading financial services organization. The work focuses on designing real-time conversational intelligence that detects customer needs, objections, and buying signals, then recommends the required process steps with compliance guardrails.

In this PoC-first engagement, you will set the technical direction for signal and intent understanding, model training and evaluation, and the move from offline experimentation to production-grade low-latency inference, all executed inside the client perimeter.

Role context and project scope

Own the applied AI architecture and evaluation strategy for a complex enterprise AI program at Staff/Architect level.
Lead the AI architecture for a long-term In-Call Assistant initiative that supports front-office employees during live customer conversations.
Design a system that identifies customer needs, objections, buying signals, and required process steps, delivering concise, context-aware recommendations.
Cover end-to-end system design including low-latency signal detection, context preparation, specialist recommendation generation, RAG over approved product and policy knowledge, confidence management, and compliance guardrails.
Help define how the AI architecture, models, evaluation framework, and feedback loops evolve from an initial offline version to live production use.

Responsibilities

Lead the applied AI architecture across the In-Call Assistant lifecycle, from data and taxonomy design to model training, evaluation, and production readiness.
Design the end-to-end AI architecture for the In-Call Assistant.
Define signal and trigger taxonomies for live conversations.
Design training strategies for signal detection and specialist recommendation models.
Shape data preparation, annotation, and SME validation workflows.
Evaluate fine-tuning, post-training, RAG, and hybrid approaches.
Design low-latency signal detection, routing, context preparation, and confidence management.
Design evaluation frameworks, golden datasets, and model improvement cycles.
Define grounding, guardrails, abstention, and policy-compliance behavior.
Make trade-offs between model quality, latency, cost, explainability, and governance.
Partner with AI engineers, data engineering, MLOps, and client SMEs.
Define the AI approach for the conversation intelligence PoC, including event/intent/insight taxonomy and golden dataset strategy and annotation workflow.
Establish evaluation frameworks and acceptance criteria, driving trade-offs between accuracy, explainability, latency, cost, and governance.
Decide which modeling approaches are appropriate for each use case.
Act as an escalation point for AI architecture, evaluation, and data strategy decisions.
Work within a cross-functional team spanning AI engineering, data engineering, MLOps, solution architecture, and client stakeholders.
Partner with domain SMEs on taxonomy, labeling, and validation.
Mentor engineers working on extraction, evaluation, and data pipelines.
Translate ambiguous business use cases into testable AI hypotheses and validation plans.

Requirements

Strong hands-on experience with applied AI/ML systems in production-oriented environments.
Experience with NLP, conversational AI, or transcript-based intelligence systems.
Ability to design evaluation frameworks, not just run experiments.
Experience building or validating structured datasets from unstructured text.
Strong understanding of LLM-based extraction, classification, RAG, and fine-tuning trade-offs.
Practical knowledge of classical ML or predictive modeling.
Understanding of probability-based prediction, calibration, and outcome evaluation.
Comfort working with messy enterprise data and incomplete labels.
Ability to communicate with both technical teams and business stakeholders.
Strong ownership of ambiguity, scope control, and PoC validation strategy.
Financial services domain exposure.
Experience with sales, call center, or customer conversation analytics.
Speech/ASR pipeline familiarity.
Model governance and auditability experience.
Experience with real-time AI systems or low-latency inference.
Experience combining unstructured conversation signals with structured CRM, transaction, or customer profile data.
Experience designing golden datasets and SME review workflows.

Technologies

LLM, SFT, DPO, preference optimization, LoRA, QLoRA, PEFT
PyTorch, Hugging Face
RAG, embeddings, retrieval, MLOps

Technology landscape

LLM and smaller-model training for conversational AI
SFT, DPO / preference optimization, LoRA / QLoRA, and PEFT
PyTorch and Hugging Face ecosystem
Signal extraction and multi-label classification
RAG and knowledge-grounded recommendation generation
Embeddings, retrieval, and context preparation
Low-latency model serving and inference optimization
Golden dataset creation, annotation, and SME validation
Model evaluation, confidence calibration, and error analysis
MLOps, monitoring, feedback loops, and model governance

Delivery and work environment

Engagement structure: FTE-equivalent via long-term B2B contract.
Work location: On-site or closely aligned with the client team in New York.
Infrastructure: Client environment only, with no external training or data processing environments.
Data residency: All work executed within the client perimeter.
Delivery mode: PoC-first, with a path toward production-grade conversation intelligence and prediction systems.

Location: New York, NY (onsite)

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2d ago

AI Engineer

Wells Fargo Charlotte, NC

Charlotte, NC

2d ago

Contract Agentic Ai Ai Agent Ai Workflows

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Job Description

Wells Fargo is hiring an AI Engineer for a contract role in Charlotte, NC with a hybrid schedule (550 S Tryon St). This position focuses on designing, building, and deploying production-ready generative AI and RAG solutions for business process automation across collateral and lending workflows in a regulated environment with governance and risk controls. The team will collaborate across business and technology groups to identify and implement practical AI use cases while supporting operationalization through testing and monitoring.

What you’ll do

Design and implement AI agents and multi-step agentic workflows to automate business processes.
Develop retrieval-augmented generation (RAG) solutions using enterprise knowledge sources.
Integrate AI agents with internal applications, databases, and APIs.
Build production-grade applications using large language models (LLMs).
Create reusable prompts, workflows, and AI components that can be applied across solutions.
Monitor and improve accuracy, reliability, and performance of deployed agents.
Collaborate with business and technology teams to identify and implement AI use cases tied to collateral and lending workflows.
Support deployment, testing, and operationalization of AI solutions.
Ensure compliance with security, privacy, and governance requirements.
Implement monitoring, traceability, and responsible AI controls.
Participate in model governance and validation activities.

What you bring

Authorization to work for ANY employer in the U.S.; not eligible for visa sponsorship.
5+ years of experience in software engineering, data engineering, machine learning, artificial intelligence, or related technical disciplines.
2+ years of hands-on experience developing Generative AI solutions.
Proven experience designing, building, and deploying AI agents or agentic workflows.
Strong development experience in Python, plus SQL and data querying skills.
Hands-on experience with LLMs and related AI technologies.
Experience building APIs and integrating enterprise applications and data sources.
Experience with Git and modern software development practices.
Strong problem-solving, analytical, and solution-design capabilities.
Excellent communication and stakeholder management skills.
Preferred: Banking, Commercial Lending, Financial Services, or Collateral Management experience.
Experience with agent frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or similar platforms.
Experience designing and implementing RAG architectures.
Knowledge of data governance, metadata management, and data quality frameworks.
Exposure to semantic technologies including Ontologies, Knowledge Graphs, RDF, OWL.
Experience supporting AI solutions in highly regulated environments.

Benefits

Health Insurance
Life insurance
401K
Voluntary Benefits

Role details

Job type: contract
Work engagement: W2
Schedule: hybrid
Duration: 12 months
Location: 550 S Tryon St, Charlotte, NC

Technologies you may use

Python
SQL
Large Language Models (LLMs)
APIs
Git
Retrieval-augmented generation (RAG)
LangChain
LangGraph
CrewAI
AutoGen
Ontologies
Knowledge Graphs
RDF
OWL

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2d ago

Senior Manager, AI Engineer

Coca-Cola Atlanta, GA

Atlanta, GA

2d ago

$152k - $178k/yr Full time Manager Agentic Ai Agentops

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Job Description

At Coca-Cola, this onsite Senior Manager role focuses on designing, building, deploying, and operating production-grade AI solutions and AI agents across the company’s digital product portfolio in cloud environments. You will work hands-on with GenAI and agentic frameworks, applying MLOps/LLMOps/AgentOps practices to connect enterprise systems, monitor performance, and enforce safety controls.

Location: Atlanta, GA (onsite). Employment: All applicants must be currently authorized to work in the United States on a full-time basis and must not require Coca-Cola Company sponsorship.

What you’ll do

Develop and deploy AI agents and GenAI solutions by prototyping, iterating, and moving domain-specific agents into production for information gathering, insight generation, and intelligent action.
Design AI agents using open interoperability standards such as Model Context Protocol (MCP) and Agent-to-Agent (A2A) to securely connect agents with enterprise data, tools, and external systems, enabling coordinated multi-agent workflows across business domains.
Write production-grade AI code by producing well-tested, maintainable engineering outputs in Python and other relevant languages.
Optimize AI models and inference pipelines for performance, reliability, and cost efficiency at scale.
Deploy and operate AI solutions on Azure, including monitoring and ongoing optimization of AI agents and models.
Build and maintain MLOps pipelines that cover model training, versioning, inference, and CI/CD.
Ensure high availability, scalability, and end-to-end observability for AI products running in production.
Integrate AI capabilities into enterprise platforms by collaborating with Application Engineering and Data Engineering squads to embed AI outputs into product workflows, APIs, and user-facing features.
Implement AI observability and telemetry for production AI agents and LLM applications, including tracing, reasoning paths, token consumption, latency, cost, output quality, and guardrail violations.
Build agent evaluation frameworks, including evaluation pipelines, harnesses, benchmark datasets, regression tests, and automated quality scoring.
Engineer enterprise AI context with retrieval pipelines using enterprise semantic layers, knowledge graphs, vector search, and business ontologies.
Implement AI safety and runtime controls such as policy enforcement, human-in-the-loop workflows, autonomy thresholds, prompt injection defenses, and secure tool execution.
Design and orchestrate multi-agent systems for coordinated planning, reasoning, tool execution, and human collaboration.
Operate LLM and agent-powered applications using modern LLMOps and AgentOps practices, including prompt versioning, evaluation, experimentation, routing strategies, and production lifecycle management.
Support enterprise digital twin capabilities by integrating operational, commercial, and enterprise data into simulations, predictions, and decision-support workflows.
Collaborate within agile teams by contributing to sprint planning, backlog refinement, technical design, code reviews, and cross-squad problem-solving.
Maintain technical currency through continuous learning and integration of advances in AI, machine learning, and Generative AI.
Conduct rigorous testing and validation to support reliability, accuracy, and explainability of AI agents and outputs.
Share knowledge through internal documentation, code reviews, and engineering best practices.

What you bring

Bachelor’s or Master’s degree in Computer Science, AI/ML, Data Science, Software Engineering, or a related technical field.
3 to 5+ years of hands-on AI or ML engineering experience with a track record of taking AI models and solutions from development to production.
Strong proficiency in Python, with working knowledge of additional languages such as Java or C++.
Experience building and deploying LLM-powered and agentic AI applications in production using frameworks such as LangChain, LangGraph, CrewAI, Semantic Kernel, Model Context Protocol (MCP), Agent-to-Agent (A2A), or similar.
Cloud experience deploying and operating AI/ML solutions with Azure strongly preferred (AWS or GCP acceptable).
Experience with vector databases, embeddings, RAG, GraphRAG, semantic search, or context engineering.
Experience developing RESTful APIs or integrating AI into enterprise applications.
Experience with MLOps/LLMOps/AgentOps, including model and prompt versioning, evaluation, experimentation, routing strategies, observability, cost optimization, and production lifecycle management.
Strong software engineering fundamentals, including API design, testing, version control, CI/CD, containerization with Docker and Kubernetes, and AI system deployment and monitoring.
Experience in Agile delivery environments, including sprint execution, code review, and cross-squad collaboration.
Experience implementing responsible AI practices such as runtime guardrails, AI safety controls, model explainability, data privacy, and secure agent execution.
Experience implementing AI observability and telemetry for tracing, reasoning diagnostics, token consumption, latency, cost, output quality, and runtime performance.
Strong analytical and problem-solving skills, including translating business requirements into well-scoped AI solutions with Technical Leads and Product teams.
Excellent communication skills to explain AI products and trade-offs to technical and non-technical stakeholders.