GenAI Engineer

• Own the end-to-end development lifecycle of GenAI and AgenticAI solutions, from experimentation and prototyping through deployment and monitoring.
• Build intelligent systems that combine time-series modeling, signal processing, and GenAI technologies including LLMs, embeddings, agents, orchestration frameworks, and retrieval pipelines.
• Design and implement LLM-powered workflows such as RAG pipelines, tool usage, multi-agent orchestration, and evaluation frameworks at scale.
• Develop AgenticAI applications that integrate diverse data sources, including sensor-based time-series data, unstructured text, and machine learning outputs.
• Drive technical decision-making across architecture, tooling, experimentation strategy, and deployment patterns for AgenticAI systems.
• Partner closely with Product, Engineering, Applied AI, and domain experts to translate customer problems into scalable technical solutions.
• Contribute to the development of data pipelines, deployment infrastructure, evaluation frameworks, and monitoring systems across MLOps and LLMOps environments.
• Build and maintain backend services in Python, including REST/gRPC APIs and workflow orchestration services connecting AI agents with platform infrastructure.
• Monitor deployed systems for performance, drift, reliability, latency, and cost efficiency, continuously improving model quality and operational scalability.
• Collaborate directly with customers and internal stakeholders to prototype and deliver innovative AI-driven experiences.
• Help shape Augury’s AgenticAI platform and user experience for industrial operators and reliability teams.

What You Bring

• Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related technical field (B.Tech / B.E. or equivalent).
• Master’s degree (M.Tech or equivalent) is a plus, but not required.
• Equivalent practical experience will also be considered for exceptional candidates.
• 2–4 years of experience spanning Data Science, Machine Learning, AI Engineering, or GenAI development.
• Hands-on experience building and deploying GenAI or AgenticAI applications in production environments.
• Strong experience with GenAI frameworks such as LangChain, CrewAI, AutoGen, LangGraph, or similar ecosystems.
• Experience implementing LLM-based workflows including prompting, embeddings, RAG, tool calling, orchestration, and evaluation systems.
• Familiarity with evaluation methodologies such as HITL, LLM-as-a-judge, deterministic evaluation, and fine-tuning workflows.
  • Experience with observability and experimentation tooling such as LangSmith or equivalent platforms.