Senior AI Engineer (GenAI + Data Platform - AWS)

Role: Senior AI Engineer (GenAI + Data Platform - AWS)

Duration:6-12+ Months Contract

Location: 2-3 days / week in the client''s Irvine office, 1 day in their downtown LA office, 1 day remote...

Number of days onsite - 4 days

Must Have Skills

• Skill 1 - Generative AI / LLM (RAG, embeddings, prompt engineering)
• Skill 2 - AWS Cloud (OpenSearch, Neptune, DynamoDB, ElastiCache/Redis)
• Skill 3 - Vector Search & Retrieval Systems (OpenSearch / vector DB)
• Skill 4 - Graph Databases (Amazon Neptune, knowledge graphs)
• Skill 5 - LLM Frameworks (LangChain / LlamaIndex)
• Skill 6 - Agentic AI Frameworks (LangGraph / AutoGen / CrewAI)
• Skill 7 - Databricks & Apache Spark (data pipelines, embedding pipelines)
• Skill 8 - Backend/API Development (Python, scalable APIs, microservices)

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Domain Experience (If any) -

• AI/ML Platform Engineering
• Generative AI / LLM Applications
• Data Platform / Big Data Engineering

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Must Have Certifications -

• AWS Certification (Preferred):
• AWS Certified Solutions Architect OR
• AWS Certified Machine Learning Specialty OR
• AWS Data Engineer Certification

Job Description: Senior AI Engineer (GenAI + Data Platform - AWS)

Role Summary

We are seeking a Senior AI Engineer to design, build, and scale a production-grade Generative AI and Data Platform on AWS. The role focuses on enabling LLM-powered capabilities through vector search, graph-based knowledge systems, and governed data pipelines.

The ideal candidate will own end-to-end delivery across the AI lifecycle, including:

Data ingestion and knowledge curation

Embeddings and retrieval systems

Backend services and APIs

CI/CD pipelines and deployment

This role will closely partner with product and engineering teams to operationalize AI capabilities in externally facing applications and drive evolution toward agentic AI systems.

Key Responsibilities

1. GenAI Enablement & Integration

Build and operationalize LLM-powered applications using:

Retrieval-Augmented Generation (RAG)

Embeddings pipelines

Prompt orchestration and evaluation frameworks

Design and implement vector search systems using Amazon OpenSearch

Develop graph-based knowledge systems using Amazon Neptune for relationships, lineage, and explainability

Integrate supporting infrastructure:

Amazon ElastiCache (Redis) for session state and caching

DynamoDB for scalable, low-latency data access

Implement agentic workflows using frameworks such as:

LangGraph, AutoGen, CrewAI (or equivalent)

Integrate with LLM frameworks like:

LangChain, LlamaIndex (tool calling, retrieval orchestration, context management)

Define standards for:

Tool integration

Context-sharing patterns (MCP-style designs)

Evaluate LLM models and retrieval strategies across:

Latency

Cost

Accuracy

Context limitations

2. Data Pipelines & Knowledge Engineering

Design and build scalable data pipelines using Databricks and Apache Spark

Implement:

Data ingestion and transformation pipelines

Document processing (chunking, metadata tagging)

Embedding generation and indexing

Ensure high data quality standards:

Validation, completeness, consistency, monitoring

Implement data governance frameworks:

Data classification and access controls

Retention policies

Auditability and lineage tracking

3. Backend Services & APIs

Develop backend services exposing AI capabilities through secure and scalable APIs

Define best practices for:

API contracts and versioning

Reliability (retry logic, circuit breakers, idempotency)

Enable reusability of platform capabilities across teams and applications

4. Deployment, MLOps & Operational Excellence

Build and manage CI/CD pipelines for AI and data workloads

Deploy production systems using:

Docker (containerization)

Kubernetes (orchestration)

Implement deployment strategies:

Blue/green deployments

Canary releases

Rollback strategies

Feature flags

Ensure system reliability through:

Monitoring (latency, failures, cost, data freshness)

Alerting and observability

Secrets management and least-privilege access

Optimize platform performance and cost

5. LLM Observability, Evaluation & Quality

Define and track GenAI quality metrics:

Grounding / faithfulness

Retrieval relevance

Response consistency

Latency and cost per request

Implement:

Prompt/version tracking

Offline evaluation pipelines

Continuous improvement workflows

6. LLM Security, Safety & Compliance

Implement secure AI systems with:

Access control and authentication

Data protection policies

Responsible AI guardrails

Ensure compliance with best practices in:

AI safety

Data privacy

Monitoring and auditability

Required Skills

Strong experience in Generative AI / LLM systems (RAG, embeddings, prompt engineering)

Hands-on experience with AWS ecosystem

Expertise in:

OpenSearch (vector search)

Neptune (graph databases)

DynamoDB and Redis (ElastiCache)

Experience with:

LangChain / LlamaIndex

Agentic AI frameworks (LangGraph, AutoGen, CrewAI)

Strong programming skills (Python preferred)

Experience with Databricks and Apache Spark

Solid understanding of:

Data pipelines

Distributed systems

API design

Preferred Skills

Experience with:

Model evaluation frameworks and LLM observability tools

AI governance and compliance frameworks

Kubernetes and advanced MLOps practices

Familiarity with:

Model Context Protocol (MCP) patterns

Agent-based architectures

Qualifications

Bachelor''s or Master''s degree in:

Computer Science / Data Science / AI / related field

Proven experience building production-grade AI platforms and systems

Strong background in end-to-end AI/ML lifecycle delivery

Soft Skills

Strong problem-solving and analytical thinking

Ability to communicate complex AI concepts clearly

Collaborative and cross-functional mindset

Ownership-driven and proactive execution