Sr. Applied AI Engineer

AssetWatch is a company that powers manufacturing uptime through condition monitoring solutions. They are seeking a Senior Applied AI Engineer to design and build reusable AI systems, focusing on prototyping LLM and agent-based workflows while collaborating with data science teams.

Responsibilities:

• Design and build end-to-end LLM-powered workflows, including RAG pipelines, tool-calling systems, and agent architectures
• Rapidly prototype internal AI assistants and automation tools across business functions
• Integrate data science models into agent-based workflows
• Translate ambiguous ideas into practical, working systems
• Develop shared connectors to major LLM providers and internal data sources
• Create reusable agent templates and AI development patterns
• Design modular systems with clean API boundaries for production handoff
• Implement lightweight evaluation, logging, and tracing patterns appropriate for internal deployment
• Partner deeply with specialty data science teams to operationalize models
• Work closely with the Head of AI to shape technical direction and AI strategy
• Communicate architectural decisions and tradeoffs clearly to technical and non-technical stakeholders

Requirements:

• BS in Computer Science, Engineering, Mathematics, or related field required; MS/PhD preferred but not required if experience demonstrates equivalent capability
• 6+ years of professional software or machine learning engineering experience building backend or distributed systems
• Strong proficiency in Python, including experience building modular, testable, and well-structured codebases
• Proficiency in SQL expected, including intermediate querying and basic ETL jobs
• Hands-on experience developing LLM-powered applications, including RAG pipelines, prompt orchestration, structured outputs, and tool-calling workflows
• Experience working with vector databases and designing retrieval strategies
• Experience designing and exposing RESTful or event-driven APIs for internal or external consumption
• Familiarity with agent frameworks or orchestration libraries (e.g., LangChain, LlamaIndex, Semantic Kernel, or similar)
• Experience integrating ML or statistical model outputs into production-oriented systems
• Understanding of evaluation concepts for LLM systems (prompt versioning, offline evals, feedback loops, or guardrails)
• Comfortable operating in ambiguity and driving initiatives end-to-end with minimal oversight
• Worked in rapid prototyping and deployment ecosystems
• Experience building internal AI tooling, SDKs, or developer enablement frameworks
• Familiarity with cloud environments (AWS preferred), containerization (Docker), and basic deployment patterns
• Experience working with streaming or ETL pipelines for ingesting structured and unstructured data
• Exposure to observability practices (logging, tracing, metrics) in application systems
• Experience handing off prototypes to production engineering teams in a clean, scalable manner