Forward Deployed Engineer

Vegapay; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Vegapay is seeking a Forward Deployed Engineer to join our fintech platform in Bengaluru. You will work at the intersection of AI/ML, production engineering, and banking partners to diagnose, design, and deploy mission-critical credit and payment programs at scale.

You will own live production issues, craft AI-enabled tooling, and drive end-to-end fixes with a strong emphasis on observability, security, and successful rollout across bank partners.

Qualifications

• 2-3 years of hands-on software engineering in B2B SaaS/fintech
• CS/AI & ML degree or equivalent
• Proficiency in Python, TypeScript, Java or Go and GitOps/LLMOps
• Experience building AI/ML apps: LLMs, embeddings, RAG, tool calling
• Strong SQL skills and understanding of REST/HTTP, JSON-RPC, GraphQL
• Experience with monitoring/observability tools and debugging distributed systems
• Cloud and Kubernetes experience on AWS/GCP/Azure
• Ability to handle high-pressure bank escalations with ownership and clear communication
• Familiarity with regulated data and guardrails in production
• High ownership and fast delivery mindset in a bank-grade environment

Responsibilities

• Lead discovery with banks, engineers, and product managers to map workflows and success metrics
• Own technical problem-solving for live fintech programs across partners
• Build agentic debugging solutions and drive root cause fixes within SLAs
• Design and deploy AI/ML solutions in production with guardrails
• Ship iteratively, then harden with monitoring, rollout plans, and observability
• Own end-to-end production changes with a robust change process and rollback plan
• Develop full lifecycle agents and harnesses with evaluation, deployment, and observability
• Lead post-incident RCAs with clear, actionable insights
• Collaborate with B2B clients to automate outage notifications and trust-building
• Incorporate field learnings into platform to enable reusable patterns

Skills

Python

TypeScript

Java

Go

LLM APIs

RAG

SQL

GitOps

LLMOps

Kubernetes

Cloud platforms

Education

B.E./B.Tech in CS or AI & ML

Tools

AWS

GCP

Azure

Kubernetes

Grafana

Prometheus

Kibana

OpenSearch

PostgreSQL

LangChain

LangGraph

CrewAI

MCP

ACP

Job description

The Impact You ll Drive

As a Forward Deployed Engineer (FDE), you ll work at the intersection of AI/ML, production engineering, product and our banking and fintech partners. You ll stay close to live systems, using code and AI to solve real fintech problems and keep mission-critical credit and payment programs running reliably at scale.

You ll work directly with partners, program teams and regulators to learn how they operate, understand their workflows, find the problems that matter most and define clear success metrics.

Then you ll deliver a solution quickly. That might be a scoped code change, an automation, a workaround, or an agentic system that stops recurring the same problem. You ll take ambiguous, high-stakes problems from first diagnosis to a production system the partner depends on, and you ll

own it after it goes live.

Success in this role requires strong engineering skills, a knack for solving operational problems, clear communication and the ability to turn ambiguity into working code quickly. You won t just recommend solutions. You ll build them and run them in production.

The Hats You Will Wear

• 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.

The Perfect Fit

• 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.

Your Edge Over the Rest

• 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.

The Problem We re Solving

Financial institutions today are held back by legacy systems that are slow, rigid, and expensive to scale. Launching or evolving credit, lending, and UPI products often takes months, requires heavy engineering effort, and limits the ability to create personalized customer experiences.

At the same time, customer expectations have changed - speed, flexibility, and tailored financial products are no longer optional. Banks and fintechs need infrastructure that allows them to innovate quickly, adapt continuously, and scale without friction.

This is where we come in.

At Vegapay, we are building modern, configurable fintech infrastructure that enables banks, NBFCs, and enterprises to design, launch, and manage credit and payment programs with ease. Our platform brings together flexibility, speed, and control - helping our partners unlock new growth opportunities and deliver personalized banking experiences at scale.

The Opportunity Ahead

• 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