Senior Machine Learning Engineer, Sumo Logic

• B.Tech, M.Tech, or Ph.D. in Computer Science, Data Science, or a related field
• 4-6 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
• 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
• Strong communication skills and a passion for shaping emerging AI paradigms

Desirable

• As a Machine Learning Engineer, you’ll build the intelligence behind the next generation of agentic AI systems and related AI systems that reason over massive, heterogeneous log data
• You’ll combine machine learning, prompt engineering, and rigorous evaluation to create autonomous AI agents that help organizations understand and act on their data in real time
• 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 context engineering, 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