Amica Financial Technologies - Director - Engineering - Artificial Intelligence

Job description
About the job:- You have personally built and shipped an AI agent in production.- Not architected it on a whiteboard.- Not owned the roadmap for one.- You wrote the code.- You designed the prompts and the tool layer.- You built the retrieval.- You ran the eval harness.- You shipped it to real users and you held the pager when it broke.- That is the qualifying condition for this role.- Everything else in this document is for people who pass that bar.- If you do not pass it, no amount of seniority, brand-name companies, or AI-adjacent engineering leadership makes up for it.- We will know within ten minutes of an interview.- Save us both the time.If you do pass it, this is the role you have been waiting for.The bet:Every fintech in India is shipping an AI feature.We are building the AI company.- Multiple production agents are already live at Jupiter, doing real work every day across the business, internal and customer-facing.- They were built solo at the leadership level because the role we are now hiring did not yet exist in Indian fintech.It does now.- The thesis is straightforward and large.- Every customer gets an agent advocate.- Every employee gets an agent peer.- The agents talk to each other through a shared trust and tool layer, composing into answers and actions no single agent could deliver.- The competitive moat is not any one agent.- It is the topology between them.- The company that nails this owns the next decade of fintech.- We intend to be that company.The role:- You will own the agent runtime, the agent platform, and the engineering-side agents.- You will not be a manager of managers.- You will design how five, then fifteen, then fifty AI agents share retrieval, auth, evals, and the ability to call each other.- You will spend roughly half your week writing code and reviewing PRs, and the other half deciding what not to build.- You will treat each agent as a production system with SLOs, on-call rotations, deploys, and post-mortems.- You will own the engineering agents that change how Jupiter ships software.- You get to build agent infrastructure from the substrate up at production scale, this is it.The team youll work with: Small, Young, Fast, Default-AI.- Most of the people you will work with on this are in their twenties or early thirties.- The defaults are build it yourself, ship it tonight, fix it tomorrow.- Meetings are short.- Documents are shorter.- The right hire treats this as a feature, not a compromise.- You will be the most senior engineer in the room and you will not act like it.- You will move at the pace they move, and you will earn their respect by writing code alongside them, not by holding architecture reviews.- If you have spent the last few years building scaffolding around yourself, you will struggle here.- If the thought of working shoulder to shoulder with a 26-year-old who ships faster than you on half the things you used to be best at makes you nervous, this is not the role.- If it sounds like the most fun you could be having, read on.What makes this different from the same title anywhere else:- Its a fleet, not a feature.- Most Director of Engineering (AI) roles today are shipping LLM features inside one product.- You will own a topology.- A customer agent grounded in a CS-knowledge agent, powered by a code-knowledge agent that ships its own PRs, all riding on a data-access agent.- That shape does not exist anywhere else in Indian fintech yet.- You will build platform, not just product.- The shared mesh, auth, retrieval, eval, and observability that lets us add a new agent in a week instead of a quarter.- This is the highest-leverage engineering surface in the company.- Your work compounds.- Every agent you ship makes the next agent cheaper.- Every eval signal you industrialize makes every future model upgrade safer.- Every PR your code-knowledge agent ships frees up an engineer to think about something only humans should think about.- It is regulated infrastructure, and that is a feature.- Two RBI-regulated entities sit underneath the runtime.- The moat that comes with doing it right is real.What youll own:The customer-facing agent runtime:- Sub-second response budgets on streaming token loops.- Multi-model routing with graceful fallback when a provider degrades.- Prompt caching that survives schema changes.- Structured output enforcement, tool-call validation, and retry policies that do not double-spend tokens.- Vector retrieval with hybrid search and reranking, tuned for Indian-language and financial-domain edge cases.- All of this on async Python, Postgres with pgvector, Redis, and EKS, with first-class tracing across every LLM call, every tool invocation, and every retrieval hop.The code-knowledge agent:- Indexing of the engineering org.- Code-aware chunking that respects symbol boundaries, dependency graphs, and recent commit history.- The agentic loop that reads, plans, edits, runs tests, and opens a PR with the context a human reviewer actually needs.- You will expand it from one allowlisted repo to a meaningful share of every PR the company ships.The data-access agent layer:- The substrate every other agent calls when it needs to know something true about the business.- Safe SQL generation against governed schemas.- Result-set reasoning.- Query plan awareness.- Caching that distinguishes stale is fine from must be fresh.The shared agent platform:- MCP-based tool exposure that makes adding a new capability a config change, not a deploy.- A unified retrieval substrate.- Eval pipelines that catch regressions before they ship.- Shared auth, secrets, and consent boundaries that hold up under audit.- Tracing that lets you debug a bad answer across three agent hops in under a minute.- Adding the next agent should take a week, not a quarter.Evals and safety:- Eval infrastructure that goes well beyond LLM-as-judge.- Golden sets for every agent skill, automated regression on every prompt change, prompt-injection red-teaming as part of CI, response-quality scoring tied back to user signals.- Today this fires on every thumbs-down.- You will turn it into the substrate every model upgrade rides on.What were looking for, beyond the qualifying filter:Distributed-systems chops in a streaming, LLM-shaped world:- Python, Postgres with pgvector, Redis, Kubernetes, async, tracing.- You have debugged why a streaming response stalled at token 47, why a tool call dropped a parameter, why a retrieval result was technically correct and operationally wrong, why a secret did not sync, why a deploy lost an environment variable.- This is not a notebook.Agent-engineering taste:- Real opinions on MCPs vs raw tool calls, classifiers vs regex, grounding vs reasoning, prompt caching vs context window stuffing, structured outputs vs free-form, planning loops vs constrained chains, evaluator agents vs deterministic checks, when a smaller faster model is the right answer and when it is a footgun.- All formed from things that broke on you in prod.Platform instinct:- You design for the fifth agent when you are building the first.- You see the auth pattern as the moat, not the inconvenience.- You build the tracing before you build the feature, because you know what it costs not to.Cost and latency fluency:- You know the per-token cost of every model you reach for and the cache hit rate of every prompt you write.- You have shipped the optimization that cut a per-query cost 40 percent without a quality regression.- You treat inference cost as a first-class metric.Operator-grade AI fluency:- You and the Director of Product should be able to argue about a single line of system prompt at the same level of detail.Builder mentality:- You will write code in week one and you will keep writing code through year one.Ambition that matches the bet:- You see this as the chance to build agent infrastructure that the rest of the industry will copy.- You are not here to add an LLM call to an existing service.Comfort with a small, young, AI-first team:- You see the team is small as freedom, not as understaffing.- You ask can an agent do this before you ask who can we hire to do this.- You take energy from engineers moving faster than you expect, not threat.What you wont do:- Sit through six-hour roadmap meetings.- Sign off on what your team builds without reading the diff.- Treat AI as separate from the rest of engineering.- Build process scaffolding to feel senior.- Spend any time defending why AI matters.The bet is made.What success looks like at six months:- All agents share a common platform.- Adding the next agent takes a week, not a quarter.- The code-knowledge agent ships its own PRs at a measurable rate per week.- Production incident rate on the customer-facing agent is below an agreed threshold, proven with telemetry you built.- Per-query inference cost on the customer agent is down meaningfully against a day-one baseline.- Leadership is no longer on-call for any agent.- The team that was small and fast when you joined is still small and fast.The basics:Experience: 8 to 10 years, no more.- We are looking for engineers in the prime building years of their career, not veterans who have stopped touching the keyboard.Compensation: 1Cr to 1.5Cr cash, plus an equity package sized to be material at exit.- We pay at the top of the Indian fintech market for this role.- If you are exceptional, we will go higher.- We will be specific about valuation, strike, vesting, and the math in the conversation.- We do not believe in we will figure that part out later.Location: Bangalore, in person.This is not a remote role.Reporting: Reports to company leadership for the first six months by design, transitioning to peer-of-executive as the handoff completes.