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Mid-level ML role in a metro with broad LLM-engineering requirements and a popular title, raising competition.
Agentic LLM production and ML infrastructure expertise create high domain specificity, limiting cross-industry transferability.
Explicit years, mandatory ML/LLM production experience, and specific tech stack enforce high shortlisting strictness.
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Lead technical architecture and design for agentic AI systems, making key decisions on model selection, frameworks, and build-vs-buy tradeoffs.
Own end-to-end agentic platform features such as multi-step reasoning, tool use, subagent orchestration, agent memory, and autonomous loops including security aspects.
Establish reusable platform components (tool/skill surfaces, context strategies) and drive performance, cost, and reliability optimizations across system layers.
B.E./B.Tech in Computer Science or related field.
5–8 years software engineering and architecture experience, including at least 3 years designing and deploying ML/LLM-based systems and 12+ months building agentic or LLM-powered systems in production.
Proficient in Python, FastAPI (or equivalent), PyTorch, distributed systems fundamentals, large datasets and ML pipelines (Ray, Spark, or equivalent).
Experience with containerized microservices (Docker, Kubernetes) and CI/CD tools (Git, Jenkins, Jira). Work Experience Required: 5–8 years as specified. Location Requirement: Based in Pune office.
Proven systems thinker capable of abstracting business domain problems into reusable platform building blocks and leading technical direction organizations follow.
Experienced in full lifecycle ownership of complex AI systems from architecture through production reliability and incident management.
Fluent in modern agentic engineering practices including context engineering, prompt caching, structured outputs, evaluation methodologies, and security design for non-deterministic AI systems.