Senior AI Engineer

Work Location:

• Onsite Requirement: Yes
• 4 days onsite
• 2 3 days/week in the client's Irvine office
• 1 day/week in the client's downtown Los Angeles office
• 1 day remote

Job 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 partner with product and engineering teams to operationalize AI capabilities in externally facing applications and drive the evolution toward agentic AI systems.

We are looking for a highly independent senior practitioner who has successfully designed, delivered, and operationalized AI solutions in production environments and can accelerate AI transformation initiatives.

Key Responsibilities:

GenAI & Agentic AI:

• Design and deliver production-grade AI and agentic solutions.
• Build LLM-powered applications using RAG, embeddings, prompt orchestration, and evaluation frameworks.
• Design vector search solutions using Amazon OpenSearch.
• Develop graph-based knowledge systems using Amazon Neptune.
• Build agentic workflows using LangGraph, AutoGen, CrewAI, or equivalent.
• Integrate LangChain or LlamaIndex for retrieval orchestration, tool calling, and context management.
• Define standards for tool integration and context-sharing (MCP-style designs).
• Evaluate LLM models and retrieval strategies for latency, accuracy, cost, and context limitations.

Data Engineering:

• Design and build scalable data pipelines using Databricks and Apache Spark.
• Develop data ingestion, transformation, document processing, embedding generation, and indexing pipelines.
• Ensure data quality through validation, monitoring, consistency, and completeness.
• Implement data governance, access controls, retention policies, auditability, and lineage tracking.

Backend Development:

• Develop secure and scalable backend services and APIs.
• Define API standards, versioning, reliability, retry logic, circuit breakers, and idempotency.
• Build reusable platform services.

Deployment & MLOps:

• Build and manage CI/CD pipelines.
• Deploy using Docker and Kubernetes.
• Implement blue/green deployments, canary releases, rollback strategies, and feature flags.
• Monitor platform performance, reliability, observability, security, and cost optimization.

AI Quality & Governance:

• Define and monitor GenAI quality metrics, including grounding, retrieval relevance, response consistency, latency, and cost.
• Implement prompt/version tracking and evaluation pipelines.
• Ensure AI security, access control, responsible AI guardrails, data privacy, and compliance.

Required Skills:

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)

Additional Required Skills:

• Strong experience building production-grade Generative AI solutions.
• Strong Python programming skills.
• Experience with distributed systems, API design, and scalable backend development.
• Experience operationalizing AI platforms and end-to-end AI/ML lifecycle delivery.

Preferred Skills:

• Model evaluation frameworks and LLM observability tools.
• AI governance and compliance frameworks.
• Kubernetes and advanced MLOps practices.
• Model Context Protocol (MCP) patterns.
• Agent-based architectures.
• Experience with content, marketing, publishing, knowledge management, or document-centric workflows.

Qualifications:

• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, or a related field.

Domain Experience:

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

Preferred Certifications:

• AWS Certified Solutions Architect
• AWS Certified Machine Learning Specialty
• AWS Data Engineer Certification

Soft Skills:

• Strong problem-solving and analytical thinking.
• Excellent communication and stakeholder management.
• Ability to work in ambiguous environments with minimal oversight.
• Strong ownership, execution, and cross-functional collaboration.
• Ability to translate business problems into AI-enabled solutions and measurable outcomes.
• Experience leading initiatives and influencing stakeholders.