AI Engineer

Description Remote Our client seeks an AI-focused engineer to build backend services and AI agents that execute end-to-end business workflows. The role will design and deploy retrieval-augmented generation systems for AI-driven lookups across enterprise data and integrate agents with secure, scalable services. The engineer will evaluate agent and RAG tooling, implement observability and guardrails, and partner with platform teams to deliver governed, reliable solutions. The impact spans business analytics, workflow automation, and improved decision support. Due to client requirements, applicants must be willing and able to work on a w2 basis. For our w2 consultants, we offer a great benefits package that includes Medical, Dental, and Vision benefits, 401k with company matching, and life insurance. Rate: $80.00 to $90.00/hr. w2 Responsibilities • Build AI agents that plan and execute business workflows with multi-step actions, including understanding requests, retrieving context, taking actions, verifying results, and logging outcomes. • Translate ambiguous business problems into repeatable agent workflows with tool and function calling, orchestration logic, and structured outputs. • Design and implement RAG pipelines across structured and unstructured sources, optimizing retrieval quality and grounding. • Implement retrieval strategies that support AI-driven database lookups using semantic search, metadata filters, and business rules. • Integrate agents into backend services via APIs and microservices, delivering reliable endpoints and orchestration layers with auditability and operational controls. • Partner with data and platform teams to ensure scalable and governed data access patterns, including caching, latency constraints, access controls, and logging. • Assess and recommend agent and RAG frameworks and deploy solutions in production-ready architectures. • Implement observability with tracing, evaluation harnesses, and drift monitoring to improve solution quality and cost and performance. • Add guardrails for prompt injection defenses, output validation, and PII handling to align with responsible AI expectations. • Establish quality gates and fallback behaviors so agents fail safely and predictably. Experience Requirements • Hands-on experience building agentic AI solutions, including LLM tool and function calling, multi-step workflows, and orchestration patterns. • Strong data experience across structured and unstructured sources, including data modeling intuition and data readiness assessment, and collaboration with analytics and data engineering teams. • Proven experience implementing RAG in production or near-production environments, including ingestion, chunking or indexing, retrieval, reranking, and grounding. • Backend engineering fundamentals including APIs, service design, reliability patterns, and integration with enterprise systems. • Proficiency in Python and or another backend language such as Java, Go, or TypeScript, with strong software engineering practices including testing, code review, and CI or CD. Education Requirements