GenAI Engineer (AgenticAI)

TL;DR

GenAI Engineer (AgenticAI): Designing, building, and deploying scalable GenAI and AgenticAI solutions for industrial environments with an accent on LLM-powered workflows, RAG pipelines, and multi-agent orchestration. Focus on integrating time-series sensor data with unstructured text and optimizing inference performance in MLOps/LLMOps environments.

Company

A leader in Machine Health and Process Health solutions using purpose-built AI technology to help manufacturers improve production outcomes.

What you will do

• Own the end-to-end development lifecycle of GenAI and AgenticAI solutions, from prototyping to production monitoring.
• Build intelligent systems combining time-series modeling, signal processing, and LLM orchestration frameworks.
• Implement scalable LLM-powered workflows including RAG pipelines, tool usage, and multi-agent orchestration.
• Develop backend services in Python, including REST/gRPC APIs and workflow orchestration services.
• Partner with Product and Engineering teams to translate industrial customer problems into technical solutions.
• Monitor deployed systems for performance, drift, reliability, and cost efficiency to improve operational scalability.

Requirements

• 2–4 years of experience in Data Science, Machine Learning, or AI Engineering.
• Hands-on experience deploying GenAI or AgenticAI applications in production environments.
• Proficiency with GenAI frameworks such as LangChain, CrewAI, AutoGen, or LangGraph.
• Strong Python proficiency for model development, backend services, and deployment.
• Experience with LLM evaluation methodologies (HITL, LLM-as-a-judge) and observability tools like LangSmith.
• Bachelor's degree in Computer Science, Engineering, or a related technical field.

Nice to have

• Background in industrial systems, IoT platforms, digital twins, or manufacturing technologies.
• Familiarity with time-series modeling, anomaly detection, or predictive maintenance.
• Experience with streaming infrastructure (Kafka, NSQ) or data platforms (Databricks, Snowflake).
• Knowledge of knowledge graphs, context graphs, or multimodal AI systems.

Culture & Benefits

• Equity compensation through stock options.
• Paid parental leave.
• Flexible PTO policy.
• Inclusive, people-first organizational culture committed to diversity and equal opportunity.