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:
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We are seeking a Senior AI Engineer to design, build, and scale a production-grade Generative AI and Data Platform on AWS.
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The role focuses on enabling LLM-powered capabilities through vector search, graph-based knowledge systems, and governed data pipelines.
Note:
Must Have Skills:
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Generative AI / LLM (RAG, embeddings, prompt engineering)
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AWS Cloud (OpenSearch, Neptune, DynamoDB, ElastiCache/Redis)
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Vector Search & Retrieval Systems (OpenSearch / vector DB)
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Graph Databases (Amazon Neptune, knowledge graphs)
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LLM Frameworks (LangChain / LlamaIndex)
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Agentic AI Frameworks (LangGraph / AutoGen / CrewAI)
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Databricks & Apache Spark (data pipelines, embedding pipelines)
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Backend/API Development (Python, scalable APIs, microservices)
Must Have Certifications:
AWS Certification (Preferred):
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AWS Certified Solutions Architect OR
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AWS Certified Machine Learning Specialty OR
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AWS Data Engineer Certification
The ideal candidate will own end-to-end delivery across the AI lifecycle, including:
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Data ingestion and knowledge curation
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Embeddings and retrieval systems
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Backend services and APIs
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CI/CD pipelines and deployment
Key Responsibilities:
1. GenAI Enablement & Integration
Build and operationalize LLM-powered applications using:
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Retrieval-Augmented Generation (RAG)
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Embeddings pipelines
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Prompt orchestration and evaluation frameworks
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Design and implement vector search systems using Amazon OpenSearch
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Develop graph-based knowledge systems using Amazon Neptune for relationships, lineage, and explainability
Integrate supporting infrastructure:
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Amazon ElastiCache (Redis) for session state and caching
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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:
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Data ingestion and transformation pipelines
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Document processing (chunking, metadata tagging)
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Embedding generation and indexing
Ensure high data quality standards:
Validation, completeness, consistency, monitoring
Implement data governance frameworks:
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:
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Strong experience in Generative AI / LLM systems (RAG, embeddings, prompt engineering)
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Hands-on experience with AWS ecosystem
Expertise in:
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OpenSearch (vector search)
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Neptune (graph databases)
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DynamoDB and Redis (ElastiCache)
Experience with:
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LangChain / LlamaIndex
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Agentic AI frameworks (LangGraph, AutoGen, CrewAI)
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Strong programming skills (Python preferred)
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Experience with Databricks and Apache Spark
Solid understanding of:
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Data pipelines
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Distributed systems
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API design
Preferred Skills:
Experience with:
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Model evaluation frameworks and LLM observability tools
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AI governance and compliance frameworks
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Kubernetes and advanced MLOps practices
Familiarity with:
Qualifications:
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Bachelor's or Master's degree in: Computer Science / Data Science / AI / related field
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Proven experience building production-grade AI platforms and systems
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Strong background in end-to-end AI/ML lifecycle delivery.
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