Artificial Intelligence Engineer

We’re looking for an AI Engineer who can design and implement LLM-powered agents that integrate with internal systems to automate operational workflows. This isn’t a research role—it’s a building role. You’ll work at the intersection of software engineering and applied AI, creating reliable, observable, production‑grade automations that teams across Operations and Finance can trust and use daily. You’ll build agents that interact with enterprise data sources and APIs, design tool-use integrations via MCP servers, implement guardrails and human‑in‑the‑loop patterns, and ensure that everything you ship is auditable and operationally sound. This role begins as a consulting engagement with a right‑to‑hire path. What You’ll Do Design and implement LLM‑powered agents that automate operational workflows—from document processing to data validation to exception handling. Build tool‑use integrations: connect agents to internal APIs, databases, and enterprise systems via MCP servers and structured tool definitions. Implement guardrails, validation layers, and human‑in‑the‑loop patterns that ensure correctness and maintain trust in automated outputs. Partner with business stakeholders to identify high‑value automation opportunities and translate them into scoped, deliverable agent workflows. Design for observability: structured logging, decision traces, cost tracking, and clear “what happened / why” visibility for every agent action. Build reusable patterns and frameworks for agent development—prompt management, evaluation harnesses, context assembly, and output validation. Stay current on LLM capabilities, API patterns, and tooling (Claude, GPT, open‑source models) and make pragmatic recommendations on model selection and architecture. Collaborate with the architecture and engineering teams to ensure AI components integrate cleanly with the broader platform (auth, audit, data governance). What We’re Looking For 7+ years of software engineering experience, with at least 2 years of hands‑on work building LLM‑based applications or AI‑powered automation in production. Strong proficiency in Python and Java, with experience building production services (not just notebooks and prototypes). Hands‑on experience with LLM APIs (Claude, OpenAI, or similar), including prompt engineering, function / tool calling, structured outputs, and context management. Experience building LLM agents with tool‑use capabilities—MCP servers, function calling, API orchestration, and multi‑step workflows. Strong understanding of AI safety and reliability patterns: output validation, hallucination mitigation, cost controls, rate limiting, and audit trails. Practical knowledge of enterprise data sources and integration patterns (REST APIs, SQL databases, messaging systems). Excellent engineering fundamentals: clean code, testing discipline, observability, and production‑readiness. Strong communication skills; you can explain AI capabilities and limitations to non‑technical stakeholders with clarity and honesty. Nice to Have Experience with RAG (Retrieval‑Augmented Generation) pipelines, vector databases, and document processing at scale. Familiarity with evaluation frameworks for LLM outputs (automated scoring, human‑in‑the‑loop review, regression testing). Experience in financial services, operations, or control‑oriented domains where accuracy and auditability are non‑negotiable. Exposure to workflow orchestration (Temporal or similar) for managing multi‑step agent processes. Tech Environment Python and Java as primary languages. LLM APIs: Claude (Anthropic), with exposure to other providers as needed. MCP servers for tool‑use integration; REST APIs for enterprise system connectivity. AKS, PostgreSQL, SQL Server, and enterprise data stores. GitHub Actions for CI/CD; observability tooling for agent monitoring. #J-18808-Ljbffr Ampstek