Responsibilities
- Design and implement state-of-the-art ML and LLM-powered features for the Aera Platform.
- Build and own agentic workflows end to end — multi-step reasoning, tool use, subagent orchestration, and long-horizon autonomous loops — with human-in-the-loop checkpoints where the stakes demand them.
- Build the eval harness before you build the agent. Own offline and online evaluation, golden datasets, regression gates tied to prompt and model versions, and test suites that hold up under non-determinism.
- Optimize agent performance and cost across the full set of levers: context engineering and compression, prompt caching, parallel and async tool calls, model selection and routing, structured outputs, and token budgets.
- Instrument what you ship. Build the tracing and observability that lets anyone profile agent behaviour, find the bottleneck, and prove a regression — rather than argue about it.
- Design tool and skill surfaces that models can actually use correctly, including MCP servers and reusable agent skills.
- Operationalize data science: integrate models into robust pipelines, inference paths, and serverless infrastructure that survive real enterprise load.
- Treat agent security as part of the design, not a review step — tool permissioning, sandboxing, and prompt-injection resistance.
- Collaborate closely with Data Science, Engineering, and DevOps to deliver solutions others can maintain.
- Explore and integrate emerging AI techniques, and bring back a point of view on what's real and what's hype.
Requirements
- B.E./B.Tech in Computer Science, Computer Engineering, or a related field.
- 3–5 years in software engineering and architecture.
- At least 2 years designing and deploying ML or LLM-based systems, including 6–12 months on LLM-specific work.
- You architect and own complex, high-stakes systems that orchestrate multiple components — and you can point to the design docs and architectural decisions you wrote to get there. You've owned something from architecture through production reliability, not just to launch.
- You are a systems thinker. You look across business domains, find the problem that's actually being solved several times over, and abstract it into building blocks the whole platform can stand on. You step one click out from the problem in front of you and interrogate the assumption underneath it.
- You are a power user of agentic coding tools — Claude Code, or equivalent agent harnesses — with real intuition for where models are strong, where they fail, and how to tell the difference before it reaches production. You bring engineering discipline to agent-generated work: you review it, you gate it, you are accountable for it. We care that you've hit the failure modes, not that you've installed the CLI.
- You are fluent in current agentic engineering practice, not last year's. Context engineering, tool and skill design, subagent patterns, agent memory, evals and LLM-as-judge, structured outputs, prompt caching, RAG as one retrieval technique among several.
- Strong Python. FastAPI or equivalent for production services.
- Experience with large datasets, ML pipelines, and distributed systems (Ray, Spark, or equivalent).
- Hands-on with PyTorch, Hugging Face, scikit-learn, pandas.
- Containerized microservices (Docker, Kubernetes) and CI/CD (Git, Jenkins, Jira).
- Humble and adaptable about code and frameworks. LangGraph or comparable orchestration frameworks are useful; none of them are the skill.
- Excellent problem-solving, communication, and collaboration.
Nice to Have
- GoLang for high-performance components.
- Vector databases (Opensearch, Pinecone, Weaviate, FAISS, pgvector).
- Event streaming and caching (Kafka, Pulsar, Redis).
- Durable Execution platform like Temporal
- Agent observability and experiment tracking (Langfuse, LangSmith, OpenTelemetry, MLflow, W&B, DVC).
- Fine-tuning where it genuinely beats prompting and context — and the judgment to know when it doesn't.
- Multi-modal AI: text, image, and structured data in one workflow.
- Serverless AI infrastructure on AWS, GCP, or Azure.
Benefits
- competitive salary
- company stock options
- comprehensive medical
- Group Medical Insurance
- Term Insurance
- Accidental Insurance
- paid time off
- Maternity leave
- unlimited access to online professional courses for both professional and personal development
- people manager development programs
- flexible working environment
- healthy work-life balance
- fully-stocked kitchen with a selection of snacks and beverages when working from the office
Work Arrangement
On-site — Pune
Team
Team size: global team of over 400 Aeranauts. Structure: Collaborate closely with Data Science, Engineering, and DevOps
