Senior Software Engineer II -Machine Learning

Senior Machine Learning Engineer

You ll be part of a small, high-impact team shaping how AI agents understand complex machine data. This is an opportunity to work on cutting-edge LLM infrastructure and contribute to defining best practices in context engineering and AI observability.

• Design, implement, and optimize

  agentic AI components, including tools, memory management, and prompts.

• Develop and maintain

  golden datasets by defining sourcing strategies, working with data vendors, and ensuring quality and representativeness at scale.

• Prototype and evaluate

  novel prompting strategies and reasoning chains for model reliability and interpretability.

• Collaborate cross-functionally

  with product, data, and infrastructure teams to deliver end-to-end AI-powered insights.

• Operate autonomously

  in a fast-paced, ambiguous environment - defining scope, setting milestones, and driving outcomes.

• Ensure reliability, performance, and observability

  of deployed agents through rigorous testing and continuous improvement.

• Maintain a strong bias for action

  delivering incremental, well-tested improvements that directly enhance customer experience.

Required Qualifications

• B.Tech, M.Tech, or Ph.D. in Computer Science, Data Science, or a related field.
• 6+ years of hands-on industry experience with demonstrable ownership and delivery.
• Strong understanding of machine learning fundamentals, data pipelines, and model evaluation.

• Proficiency in Python

  and ML/data libraries (NumPy, pandas, scikit-learn); familiarity with JVM languages is a plus.
• Working knowledge of

  LLM core concepts

  , prompt design, and

  agentic design patterns

  .
• Experience with

  distributed systems

  and dealing with large amounts of data.
• Strong communication skills and a passion for shaping emerging AI paradigms.

Desired Qualifications

• Experience leading a team of engineers.
• Prior experience

  building and deploying AI agents

  or LLM applications in production.
• Familiarity with modern

  agentic AI frameworks

  (e.g., LangGraph, LangChain, CrewAI).
• Experience with

  ML infrastructure and tooling

  (PyTorch, MLflow, Airflow, Docker, AWS).
• Exposure to

  LLM Ops

  - infrastructure optimization, observability, latency, and cost monitoring.