Forward Deployed Engineer

Location: Bengaluru, India

Department: Client Success

• Lead discovery with bank partners, engineers, and product managers to understand operational workflows and deployment requirements, identify the real bottleneck, and define clear, measurable success metrics.
• Own technical problem-solving for live credit card, UPI, CLOU, lending tech, BRE, forex, rewards, ledgering and reconciliation programs across multiple bank partners, often juggling several escalations in flight at once.
• Build agentic debugging solutions for complex production issues across services, APIs, and data layers, and drive each issue to root cause and a permanent fix within strict SLAs.
• Design, build, and deploy AI/ML and agentic solutions in production. This includes LLM + RAG over runbooks, logs, and tickets, plus tool-calling agents (LangChain, LangGraph,CrewAI, MCP, ACP) that triage escalations, point to likely root causes, draft RCAs and
• automate recurring fixes. The goal is self healing systems.
• Ship fast, then harden. Deliver a working first version in days, then make it reliable over weeks with proper rollout plans, monitoring, and iteration based on real usage.
• Own production code changes end to end: read an unfamiliar service, make a scoped, tested fix, and ship it through a bank-grade change process: code review, audited commits, synthetic or masked data, rollback plan.
• Build AI guardrails for a regulated environment: human-in-the-loop approval for anything touching money or customer data, least-privilege access, prompt-injection and data-leakage controls, and full audit trail.
• Own the full lifecycle of the agents and harnesses you build: evaluation (groundedness, retrieval quality, tool-call accuracy, regression), controlled deployment, and observability (quality, latency, cost, drift, errors, confidence scores).
• Lead post-incident RCAs that produce clear, actionable insights. Tell a true product gap, which routes to developers apart from an implementation gap, which you own and close.
• Work directly with B2B clients (banks and fintechs) to automate near-real-time outage notifications and build trust under pressure.
• Feed what you learn in the field back into the platform and the core product, turning one-off fixes into reusable patterns so each deployment makes the next one easier.
• Operate effectively in dynamic environments with evolving business and deployment requirements.

• 2–3 years of hands-on software engineering experience in B2B SaaS, fintech or payments, with real production code and AI/ML work to your name.
• B.E./B.Tech in Computer Science or AI & ML, with strong coding, data structures and algorithms fundamentals.
• Proficiency in at least one of Python, Typescript, Java or Go; able to find your way around an unfamiliar codebase, make a scoped, tested change and ship it using GitOps and LLMOps practices.
• Hands-on experience building AI/ML applications: LLM APIs, context engineering, embeddings, RAG, structured outputs and tool calling. You’ve built or operated at least four real agentic workflows in high-stakes production (orchestration, memory, plan–act–observe loops).
• Understanding of LLM evaluation, observability and guardrails, and why they must pass before a production release.
• Strong hands-on SQL skills, including joins, aggregations and tracking down data mismatches, plus a solid understanding of how requests and responses work across REST/HTTP, JSON-RPC and GraphQL APIs.
• Experience with monitoring and observability tools (Grafana, Prometheus, Kibana, ELK/EFK, OpenSearch) and a proven ability to debug distributed-systems issues.
• Understanding of system design basics (microservices, queues, retries, idempotency, failure handling) and working knowledge of databases (PostgreSQL preferred).
• Comfortable working with at least one cloud platform (AWS, GCP or Azure) and Kubernetes.
• Proven ability to handle several high-pressure bank escalations at once, calmly and through to resolution, with strong accountability, ownership and clear stakeholder communication.

• Experience with payments, banking or fintech systems: UPI, card networks (Visa, Mastercard, RuPay), Credit Line Over UPI, Credit Card Management systems, ledger systems and reconciliation flows.
• Familiarity with NPCI’s UPI ecosystem or RBI’s digital lending guidelines, and a strong instinct for handling regulated data.
• Depth in agentic and GenAI tooling (MCP servers, LangGraph, CrewAI, embeddings, FastAPI), with an eye on monitoring and inference costs.
• You don’t just fix issues. You prevent them, and you automate recurring ones out of existence.
• AI-native: you actively use AI tools in your own work and push for an AI-first approach in production by default.
• Full ownership: a problem is yours until it’s solved in production, not just until the code is merged.
• Highest standards: shipping 80% isn’t enough for you. You care about correctness, reliability, guardrails and edge cases, because these are a bank’s production systems.

• Solve real-world fintech problems at scale with high ownership and impact
• Work directly with top banks and cutting-edge credit infrastructure
• Fast-paced, high-learning environment with strong growth opportunities