Senior Genai Engineer

Augury Augury · Vertical AI · Bengaluru India · R&D

Senior GenAI Engineer role focused on designing, building, and deploying scalable GenAI and AgenticAI solutions for industrial environments. The role involves owning the full lifecycle of GenAI applications, from experimentation to production deployment, observability, and optimization, using multi-modal industrial datasets and LLM pipelines.

What you'd actually do

  1. Own the end-to-end lifecycle of GenAI and AgenticAI solutions, from problem definition and experimentation through deployment and monitoring.
  2. Design and implement scalable AI systems leveraging:
  3. Build and optimize production-grade LLM pipelines, including RAG systems, embedding pipelines, multi-agent workflows, tool orchestration, and evaluation frameworks.
  4. Architect AgenticAI applications utilizing diverse inputs such as sensor-based industrial time-series data, unstructured text, and existing ML systems.
  5. Develop backend services and APIs in Python to support AI agents, orchestration workflows, and integrations with streaming/data infrastructure (e.g., Kafka, NSQ).

Skills

Required

  • 4+ years of experience spanning Data Science, Machine Learning, and/or Generative AI.
  • Hands-on experience building and deploying GenAI or AgenticAI applications in production environments.
  • Strong Python development skills across model development, backend services, deployment, and monitoring.
  • Experience building scalable APIs, distributed systems, and microservices.
  • Familiarity with streaming and data infrastructure platforms.
  • Experience working in Agile environments with a bias toward rapid iteration, experimentation, and continuous improvement.
  • Proven ability to collaborate across globally distributed Product, Infrastructure, Data Engineering, and R&D teams.
  • Ability to balance research innovation with pragmatic engineering execution.
  • Experience optimizing LLM/API usage and infrastructure costs.

Nice to have

  • Experience with modern AI/ML frameworks such as PyTorch, TensorFlow, Hugging Face, LangChain, CrewAI, AutoGen, and LangSmith.

What the JD emphasized

  • production environments
  • production-grade LLM pipelines
  • production systems
  • production-ready systems
  • production deployment
  • production-grade AI systems

Other signals

  • GenAI
  • AgenticAI
  • LLM
  • industrial datasets
  • production deployment