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