Senior AI Engineer

Quick Facts

• Role: Senior AI Engineer

Description

Build production-grade AI agents and workflows that convert complex scientific and regulatory content into structured, machine-readable outputs. This is a hands-on software engineering role focused on designing, implementing, refactoring, and operating reliable AI-powered capabilities in a production codebase. You’ll move beyond prototypes into maintainable, scalable systems by translating domain needs into clear processing rules and improving reliability through deep debugging.

Responsibilities

• Design, build, and enhance AI agents and workflows for structured outputs from scientific content
• Contribute production-quality Python code (new capabilities, refactoring, defect resolution)
• Analyze instruction patterns, edge cases, and ambiguous requirements and translate them into processing rules
• Build reliable services and pipelines with schema validation, error handling, logging, traceability, testing, and observability
• Investigate failures across prompts, data, orchestration, and application logic; implement durable root-cause fixes
• Collaborate with domain experts, product partners, and engineers on technical design, acceptance criteria, and documentation

Requirements

• 5+ years of professional software engineering experience with substantial hands-on Python development
• Practical experience developing applications with LLMs, NLP, machine learning, or other AI capabilities
• Strong software design skills (modular architecture, reusable components, design patterns, maintainable code)
• Experience with automated testing, code review, version control, and CI/CD practices
• Strong debugging and root-cause analysis across code, data, and distributed workflows
• Experience building agentic AI systems or multi-step LLM workflows using LangGraph, LangChain, Semantic Kernel, or comparable frameworks
• Experience building and maintaining production backend services, APIs, or data-processing systems
• Experience with structured-output generation, tool calling, prompt orchestration, state management, and model fallback strategies
• Experience with structured/semi-structured formats (JSON, XML), including schema validation and transformation
• Experience with AWS, containerized applications, infrastructure as code, and production observability
• Familiarity with LLM evaluation, golden datasets, regression testing, and quality measurement
• Domain experience in pharmaceutical, healthcare, laboratory, scientific, regulatory, or other quality-sensitive environments