About The Company

Founders today are building global companies from day one — but the systems that manage their money were built for a different era. Aspire exists to change that!

We’re building the financial operating system for global founders, bringing banking, software, and automation into a single platform so businesses can move faster across borders and stay focused on building.

Aspire is built by people who think from first principles, care deeply about solving hard problems, and take real ownership of their work. Our team brings global experience from leading fintech and technology companies, and many of us are former founders and operators who understand what it takes to build thoughtfully, make trade-offs, and deliver at scale in a global environment.

Backed by leading global investors including Y Combinator, Peak XV, and Lightspeed, Aspire has been trusted by more than 50,000 startups and growing businesses worldwide to manage their finances since 2018. Together with partners like J.P. Morgan, Visa, and Wise, we’re building for the next generation of global companies.

About The Team

Our Engineering Team is at the forefront of this transformative journey, responsible for designing, building, and maintaining Aspire's exceptional products and infrastructure. We take pride in developing cutting-edge, digital first technology solutions that simplifies financial services for businesses.

About The Role

We're hiring two Senior AI Engineers (Software Engineer III) for the OS Platform team in Gurgaon. Aspire OS is where AI becomes the way our customers run their finances: agents that reconcile, approve, pay, forecast and explain, inside a regulated product where a wrong answer can move real money.

You'll build the foundation those agents run on: the model gateway, the agent runtime, tool and MCP integrations, retrieval and memory, evaluation, and the guardrails that make agents safe enough for a financial product. This is a hands-on role. You'll ship to production, own what you ship and help set the standard for how Aspire builds with AI.

What You'll Be Doing

- Build production agents and multi-step workflows on LangGraph, LangChain or custom orchestration, with durable state, retries and human approval for money-moving actions.
- Own the LLM gateway: routing and fallback across providers (OpenRouter, AWS Bedrock and direct APIs), with cost, latency and quality budgets, caching, rate limits and per-tenant controls.
- Build MCP servers and tool interfaces that let agents act safely on Aspire's banking, cards, payments and accounting capabilities.
- Design RAG pipelines, agent memory and reusable agent skills, and manage context windows deliberately.
- Build the guardrails and policy layer: input and output validation, prompt-injection defences, PII redaction, policy-as-code, action and spend limits, and audit trails that stand up to regulators.
- Run the agent development lifecycle (ADLC) end to end: spec, eval-first build, guardrail review, staged rollout, monitoring and iteration.
- Cut latency and cost through streaming, caching, batching, parallel tool calls and model right-sizing, against p95 and cost-per-task targets you own.
- Write production Python and Go, deploy on AWS with solid CI/CD and observability, and raise the bar for the team through code and design reviews.

About You

What we're looking for

- 5 to 8 years in software engineering,



including at least 2 years building and shipping LLM-powered systems to production.
- Strong full-stack engineering: fluent in Python and at least one of Go or TypeScript, with experience designing and running backend services and APIs at scale.
- Hands-on agent building: tool use, planning, memory and multi-step orchestration with LangChain, LangGraph or equivalent, plus MCP servers or clients.
- Deep working knowledge of LLMs: model trade-offs, context windows, structured output, function calling, retrieval versus fine-tuning, and failure modes such as hallucination and prompt injection.
- Production experience with RAG and vector databases (pgvector, OpenSearch, Pinecone or similar).
- Experience running an LLM gateway or router (OpenRouter, LiteLLM or in-house), including fallback, cost control and caching.
- Experience building guardrails, policy enforcement and evaluation frameworks for AI systems.
- A track record of measurably reducing latency or cost in a production system.
- Production AWS experience with infrastructure as code, CI/CD and observability (Datadog or similar).
- Experience in fintech or another regulated industry, with working knowledge of payments, ledgers, KYC/KYB or compliance constraints.

The AI stack you'll work with You won't need every item below, but you should understand the trade-offs in each and have real depth in several.

- RAG: ingestion and chunking, embedding selection, hybrid search, re-ranking, query rewriting, grounded answers with citations, GraphRAG, and retrieval evaluation (recall@k, faithfulness).
- Agent memory: working memory within a run, long-term episodic and semantic memory, per-user and per-tenant scoping, summarisation and context compaction, and PII-secure retention.
- Agent skills: reusable skill packages (instructions, scripts and resources), progressive loading to protect the context window, skill and tool registries, versioning and testing.
- ADLC: prompts, tools and agent graphs treated as versioned, tested artefacts, released through evals, canaries and rollback.
- Orchestration: LangGraph and LangChain, state machines, multi-agent patterns (supervisor, handoff, agent-to-agent), planning and reflection, durable execution (Temporal or AWS Step Functions).
- MCP and tool use: secure MCP servers and clients, per-tool authorisation, tool schema design, function calling and structured outputs.
- Models and routing: frontier and open-weight models, reasoning versus non-reasoning trade-offs, OpenRouter and AWS Bedrock, cost-aware routing, fine-tuning and distillation where they pay off.
- Context and latency: context budgeting, prompt and semantic caching, streaming, batching, parallel tool calls, and awareness of quantisation and speculative decoding.
- Evaluation and observability: golden datasets, calibrated LLM-as-judge, regression suites in CI, tracing and cost tracking (Langfuse, LangSmith, Braintrust, OpenTelemetry), drift detection.
- Safety and policy: guardrail frameworks,



prompt-injection and jailbreak defences, PII redaction, policy-as-code (OPA or Cedar), red-teaming, immutable audit trails.
- Data and documents: vector stores, document intelligence (OCR, invoice and statement extraction) and multimodal inputs.
- AI-assisted engineering: daily use of agentic coding tools such as Claude Code or Cursor, spec-driven development, and the judgement to know when not to trust the output.

Nice to have

- Fine-tuning, distillation or self-hosting open-weight models (vLLM, Bedrock, SageMaker).
- Experience with multi-agent systems or computer-use agents.
- Familiarity with financial regulation and model risk expectations (MAS, SOC 2, PCI DSS, GDPR) as they apply to AI.
- Open-source contributions, writing, talks or side projects that show you build with AI for the love of it.

How You Work

- You execute independently. Give you an ambiguous problem and you come back with a working system. You unblock yourself and finish what you start.
- You take the initiative. You spot a problem, validate it with data and propose the fix before anyone assigns it.
- You stay curious about AI. You try new models and tools the week they ship, and tell the team what is worth adopting and what is hype.

- You are self-driven. You set your own bar and measure your own impact.
- You are a polyglot. You choose the language that fits the problem, whether Python, Go or TypeScript.
- You build safety in. You treat customer money and data with care and add guardrails from the first commit, not after an incident.

- You communicate clearly. You write short, precise design docs and explain trade-offs to engineers, product and risk partners alike.

What Success Looks Like

- First 30 days: you ship a production change to the agent platform and know the gateway, guardrail and evaluation setup well enough to review others' work.
- First 90 days: you own one platform component end to end, such as the MCP tool layer, retrieval, memory or the eval pipeline, and deliver a measurable gain in latency, cost or answer quality on a live Aspire OS agent.
- First 12 months: product teams come to you when they want to put an agent in front of customers safely, and the platform gives them paved paths they can follow on their own.

Why join

- Build the AI foundation of Aspire OS while it is still being shaped, with direct influence on what the platform becomes.
- Work on hard problems where AI meets real money: safety, correctness, latency and scale all matter at once.
- Join a team where every engineer builds with AI tools every day, inside a global fintech trusted by more than 50,000 businesses.

Disclaimer

By submitting this application, I confirm that all the information given by me in this application for employment and any additional documents attached hereto are true to the best of my knowledge and that I have not wilfully suppressed any material fact. I confirm I have disclosed if applicable any previous employment with Aspire. I accept that if any of the information given by me in this application is in any way false or incorrect, my application may be rejected, any offer of employment may be withdrawn or my employment with Aspire may be terminated summarily or I may be dismissed.

By submitting this application, I agree that my personal data will be processed in accordance with

Aspire's Candidate Privacy Notice.

📌 Software Engineer III (Sr AI Engineer) (Gurugram)
🏢 Aspire
📍 Gurugram