Senior Machine Learning Engineer II

Sumo Logic

4d • $148,000 - $173,200

About The Position

Sumo Logic is a cloud-native SaaS data analytics platform that solves complex observability and security problems. Customers choose our product because it allows them to easily monitor, optimize, and secure their applications, systems, and infrastructures. Our microservices architecture, hosted on AWS, ingests petabytes of data daily across multiple geographic regions. Millions of queries a day analyze hundreds of petabytes of data.
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.

Requirements

• 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.

Nice To Haves

• Authored research/white papers and published in conferences.
• Mentored junior engineers in guiding ML applications

Responsibilities

• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Ph.D. or professional degree