Senior AI Engineer (GenAI and Data Platform - AWS)

Role:

Senior AI Engineer (GenAI + Data Platform AWS)

Location:

4 days a week onsite is must (3 days in Irvine, CA & 1 Day in Downtown, LA, CA)

Job Type:

Contract 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.

Note:

Must Have Skills:

Generative

AI / LLM

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

Must Have Certifications:

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

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.