Sr. Applied AI Engineer

AssetWatch, Inc.

8h•Remote

About The Position

AssetWatch serves global manufacturers by powering manufacturing uptime through the delivery of an unparalleled condition monitoring experience, with a passion to care about the assets our customers care for every day. We are a devoted and capable team that includes world-renowned engineers and distinguished business leaders united by a common goal – To build the future of predictive maintenance. As we enter the next phase of rapid growth, we are seeking people to help lead the journey. We are hiring a Senior Applied AI Engineer to design and build reusable AI systems that accelerate innovation across the organization. This is a senior individual contributor role focused on rapidly prototyping LLM and agent-based workflows, architecting clean and reusable patterns, and partnering closely with our specialty data science team and Head of AI. You will translate strategy and ideas into structured, modular systems that can be handed off to production engineering teams or integrated via well-defined APIs. We expect end-to-end ownership, pragmatic tradeoffs, and a bias for action.

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

Nice To Haves

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

Responsibilities

AI Prototyping & Workflow Development
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
Reusable Architecture & Enablement
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
Cross-Functional Collaboration
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