Senior AI/ Machine Learning Engineer

Aera Technology is a pioneer in the growing category of Decision Intelligence Platforms and a Leader in the Gartner® Magic Quadrant™ for 2026 – the technology to digitize, augment, and automate decision-making processes with AI and machine learning. Through our AI decision automation platform, Aera Decision Cloud™, we are helping the best-known brands in the world make smarter, faster decisions. Privately-held and VC-funded, we have a global team of over 400 Aeranauts – and we’re growing. We deliver Decision Intelligence innovation and services that enable enterprises to automate and scale decision making with accuracy and speed. We continue to be the trusted choice of market leaders for our proven ability to generate value and unlock opportunities that were previously unattainable.    We are looking for a Senior AI/Machine Learning Engineer to set the technical direction for agentic AI on the Aera Platform. This is a role for someone who has already shipped autonomous agents into production, watched them fail in ways the demo never showed, and built the evaluation and guardrails that made them trustworthy the second time. You will make the architectural calls that the rest of the team builds on — and you will be accountable for them when a recommendation moves a real supply chain.   This role will be based in our Pune office.

Responsibilities: Set technical direction for agentic systems at Aera. Choose the architecture, write the design docs, and make the call on build-vs-buy, model selection, and framework adoption — then defend those decisions with evidence when they're challenged.

Own the agentic platform end to end: multi-step reasoning, tool use, subagent orchestration, agent memory, and long-horizon autonomous loops — including where a human checkpoint is non-negotiable and where it isn't.

Own the evaluation discipline for the org, not just your own features. Offline and online evals, golden datasets, LLM-as-judge pipelines, regression gates tied to prompt and model versions, and test suites that hold up under non-determinism. Make it the default others inherit rather than the thing you did once.

Establish the reusable substrate — shared tool and skill surfaces, MCP servers, context strategies, prompt-caching and routing layers — so common problems get solved once for the platform instead of repeatedly per team.

Drive performance, cost, and reliability across the full set of levers: context engineering and compression, parallel and async tool calls, model selection and routing, structured outputs, token budgets, and inference optimization. Know which lever to pull from the trace, not from a guess.

Raise the bar around you. Mentor engineers, review designs and agent-generated code with real rigour, and set the standard for what “production-ready” means for a non-deterministic system.

Own agent security as a design property — tool permissioning, sandboxing, prompt-injection resistance, and the blast radius of an agent that misbehaves in a customer environment.

Partner with Data Science, Engineering, DevOps, and Product to turn ambiguous business problems into systems that hold up — and push back when the framing is wrong.

Evaluate emerging AI techniques and give the organization a defensible point of view on what's real, what's hype, and what we should adopt now.

About You: B.E./B.Tech in Computer Science, Computer Engineering, or a related field.

5–8 years in software engineering and architecture , with at least 3 years designing and deploying ML or LLM-based systems and 12+ months building agentic or LLM-powered systems that reached production .

You have set technical direction others followed. You can point to design docs you wrote, architectural decisions you owned, and at least one system you took from architecture through production reliability and then lived with — including the incidents.

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 — and you can bring other people to that altitude with you.

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.

You have opinions about evaluation that you can defend. You know why an agent that passes a benchmark can still be unshippable, and you've built the harness that caught it.

Deep Python. FastAPI or equivalent for production services.

Strong distributed-systems fundamentals; experience with large datasets and ML pipelines (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.

You write and argue clearly. Senior work here is as much about bringing people to a decision as reaching it yourself.

Good to Have: Experience leading a technical workstream or mentoring engineers, formally or otherwise.

GoLang for high-performance components.

Vector databases (Pinecone, Weaviate, FAISS, pgvector).

Durable Execution platform like Temporal

Event streaming and caching (Kafka, Pulsar, Redis).

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.

Enterprise-grade constraints: multi-tenancy, data isolation, compliance, or regulated deployments.

“We need more systems thinkers, people who can look across all the business domains and abstract that to, here's the building blocks we're going to need.”

How We Work: That is the engineer we are looking for. Enterprise decision intelligence is unforgiving — when our platform recommends an action, a real supply chain moves. That constraint shapes how we hire and how we build.

Talent density over headcount. We would rather solve a hard problem with a small team of people who are excellent at what they do and better at working together, than staff around the gap. This is the non-negotiable — everything else here depends on it.

Craft still scarce, still decisive. AI has made it easy to produce code. It has not made great engineering common. There's a difference between writing lines of Python and understanding how code, systems, and products actually work — and the second one is not going away. We hire for the second one.

Context, not process. When something goes wrong, our instinct is a blameless retrospective and a smarter person, not a new approval step. We expect you to take real risks, recover fast when they don't land, and argue for the best outcome for the business rather than the safest one for you.

Comfortable in the discomfort. We are rebuilding how enterprises make decisions while the underlying technology changes under us every quarter. If ambiguity energizes you rather than stalls you, you'll do the best work of your career here.

AI fluency at every level. Not a mandate handed down — an expectation we hold for ourselves too, including for people who no longer write code.

If you share our passion for building a sustainable, intelligent, and efficient world, you’re in the right place. Established in 2017 and headquartered in Mountain View, California, we're a series D start-up, with teams in Mountain View, San Francisco (California), Bucharest and Cluj-Napoca (Romania), Paris (France), Munich (Germany), London (UK), Pune (India), and Sydney (Australia).  So join us, and let’s build this!   Aera Technology is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender perception or identity, national origin, age, marital status, protected veteran status, or disability status.   Benefits Summary  At Aera Technology, we strive to support our Aeranauts and their loved ones through different stages of life with a variety of attractive benefits, and great perks. In addition to offering a competitive salary and company stock options, we have other great benefits available. You’ll find comprehensive medical, Group Medical Insurance, Term Insurance, Accidental Insurance, paid time off, Maternity leave, and much more. We offer unlimited access to online professional courses for both professional and personal development, coupled with people manager development programs. We believe in a flexible working environment, to allow our Aeranauts to perform at their best, ensuring a healthy work-life balance. When you’re working from the office, you’ll also have access to a fully-stocked kitchen with a selection of snacks and beverages.