Senior Generative AI Engineer/AI Engineer

Overview:

Client is at the forefront of AI innovation, leveraging cutting-edge technology to transform legacy systems into modern, efficient, and scalable solutions. We work with enterprise clients to breathe life into their existing software, ensuring that they can meet the demands of today's fast-paced, digital landscape. This hands-on, non-client-facing role reports to the AppMod Specialist and plays a key part in executing modernization projects using Client AI-powered solutions. The ideal candidate will have a background in software architecture and proven experience modernizing legacy applications... We are looking for developers to create generative AI Applications.

Requirements

1. Must have 3-5 yrs hands-on experience in Machine Learning (ML) experience beyond academia, thrives in fast-paced environments, and enjoys solving complex technical challenges.
2. Experience building production LLM or multi-agent systems
3. Practical proficiency with graph databases (e.g., TigerGraph, Neo4j) and graph-based retrieval (e.g., GraphRAG), plus experience with vector databases like Opensearch, CosmosDB, or Pinecone
4. Deep experience with one or more: LangGraph, AutoGen 0.4, Llamalndex AgentWorkflow, ag2, strands agents, etc
5. Retrieval for agents: Opensearch plus TigerGraph and GraphRAG patterns for global reasoning
6. Observability: Langfuse or Phoenix with OpenTelemetry; track quality, cost, and latency
7. Serving: Deploy and scale agentic workloads via AWS Fargate, Step Functions, and Lambdas (or equivalent services on other clouds), ensuring p95 latency targets
8. Interoperability: Model Context Protocol (MCP) for tool and data access, with bonus for LangGraph MCP adapters
9. Experience with cloud environments (e.g., AWS, Azure, GCP): ECS, Step Functions, Lambda and equivalents on other clouds.

Preferred Qualifications w Experience in any of the following:

- Agent development

- Data privacy

- Fine tuning LLMs

- LLM architecture and techniques for performance

- LLMOps

- ML evaluation

- Model decay and data drift detection and handling

- lac: Pulumi, Terraform, and/or Cloud SDKs

- Security

- Vector databases