Senior AI/ Machine Learning Engineer

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.