Why This Job is Featured on The SaaS Jobs
This Senior Machine Learning Engineer role stands out in SaaS because it sits at the intersection of observability, security, and real-time analytics, domains where product value is tightly coupled to data quality and system reliability. Working with high-volume, heterogeneous log streams and agentic AI approaches signals a platform-oriented environment, where ML is embedded into core workflows rather than treated as a standalone feature.
For a SaaS career, the work maps to durable themes: production-grade LLM applications, evaluation discipline, and operational considerations like latency, reliability, and monitoring. The emphasis on context engineering, memory management, and end-to-end delivery aligns with how modern SaaS teams are integrating AI into customer-facing experiences while maintaining measurable performance. Experience gained here tends to transfer across SaaS companies building AI-assisted products, particularly those with complex data pipelines and multi-tenant operational constraints.
The position is best suited to an engineer who prefers owning ambiguous problem spaces, collaborating across product and infrastructure, and iterating through incremental, testable improvements. It also fits someone who wants to balance applied ML fundamentals with emerging agentic patterns, and who is motivated by building systems that must perform consistently in production rather than only in research settings.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
Senior Machine Learning Engineer
Location: (Bangalore or Noida)
The proliferation of machine log data has the potential to give organizations unprecedented real-time visibility into their infrastructure and operations. With this opportunity comes tremendous technical challenges around ingesting, managing, and understanding high-volume streams of heterogeneous data
As a Machine Learning Engineer, you’ll build the intelligence behind the next generation of agentic 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.
Responsibilities
- Design, implement, and optimize agentic AI components, including context engineering, memory management, and prompts.
- 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.
- 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.
- Strong communication skills and a passion for shaping emerging AI paradigms.
Desired Qualifications
- 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.
About Us
Sumo Logic, Inc. helps make the digital world secure, fast, and reliable by unifying critical security and operational data through its Intelligent Operations Platform. Built to address the increasing complexity of modern cybersecurity and cloud operations challenges, we empower digital teams to move from reaction to readiness—combining agentic AI-powered SIEM and log analytics into a single platform to detect, investigate, and resolve modern challenges. Customers around the world rely on Sumo Logic for trusted insights to protect against security threats, ensure reliability, and gain powerful insights into their digital environments. For more information, visit www.sumologic.com.
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