IT|Data Analysis - Specialist II - Data Science

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Location: CA

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

• 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

• 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

• 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

• 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

• 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

• 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

Mandatory Areas

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
  
  

______________

Must Have Certifications –

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

______________

Location – – 2-3 days / week in the client’s Irvine office, 1 day in their downtown LA office, 1 day remote…

______________

Onsite Requirement – Yes

Number of days onsite – 4 days