Sr. AI 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).

Do you have experience in Systems integration?, **Must be able to work onsite in NY 3 days a week (Tues-Thurs)

Must be able to work with no sponsorship

Top Skills

Python and Java, building LLM agents, MCP servers, and tool-use integrations against enterprise datasources and APIs.

Nice to haves

Funding and/or Finance domain experience highly preferred., · 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.