Forward Deployed Engineer

GreyLabs AI is building the voice operating system for India's BFSI. Our Agentic Voice AI platform helps banks, insurers, NBFCs, and fintechs automate and humanize millions of customer conversations - across sales, collections, customer service, and compliance - in multiple Indian languages.

In under two years, we've scaled to 50+ enterprise clients including RBL Bank, AU Small Finance Bank, IDFC FIRST Bank, SBI Life, ICICI Prudential Life, Motilal Oswal, and Groww - processing hundreds of millions of conversations. We raised ₹85 Crores in Series A funding led by Elevation Capital with Z47, and were recognized for "Best Use of AI in Fintech" at IFTA 2025.

We're hiring a Forward Deployed Engineer (FDE) to work at the intersection of engineering and customer delivery - translating enterprise business problems into production AI deployments on the GreyLabs platform.

This is not a back-office engineering role. You'll sit across the table from banking ops leads, product managers at insurance companies, and tech teams at fintechs - understanding their workflows, designing AI-powered solutions, building integrations, and owning the path from pilot to production. You'll be the person who makes GreyLabs work in the real world, across real systems, for real users.

One week you might be designing a multilingual collections voice agent for a major bank, configuring conversation flows, and integrating with their CRM. The next, you could be debugging a data pipeline issue on a live deployment, optimizing containment rates based on production conversation logs, or running a technical workshop for a client's engineering team.

This role demands equal parts technical depth and customer instinct. You'll write code, build integrations, configure platforms, and troubleshoot production systems - but you'll also need to listen well, communicate clearly, and develop a genuine understanding of how financial institutions operate. If you've ever felt that the best engineering happens closest to the customer, this role was built for you.

• Own enterprise implementations end-to-end - from understanding business workflows and designing AI use-cases, to configuring the platform, building integrations, driving pilots, and ensuring successful production rollout

• Build and configure AI workflows on the GreyLabs platform - prompts, conversation logic, routing rules, fallback handling, and automation behavior - customized by industry and use-case (collections, customer service, sales, compliance)

• Integrate with enterprise systems - CRMs, ticketing tools, dialers, internal APIs, and databases using Python, REST APIs, webhooks, SFTP/S3, and batch data pipelines

• Be the technical face of GreyLabs during implementation - run workshops, demos, and troubleshooting sessions with client engineering and ops teams; debug production issues and drive resolution

• Improve outcomes continuously - analyze conversation logs, system behavior, and performance metrics to optimize accuracy, containment, and operational efficiency across deployments

• Feed insights back to product - identify patterns across customer deployments that should become platform features, reusable templates, or accelerators for future implementations

• 3 - 5 years of professional experience in a technical role - software engineering, solutions engineering, technical consulting, or implementation engineering

• Strong Python skills - you're comfortable writing scripts, building API integrations, and handling data transformations in production contexts

• Working experience with REST APIs, webhooks, and authentication patterns (OAuth, token-based auth)

• Familiarity with data connectivity - SFTP, S3, or batch data ingestion workflows

• Ability to debug real production issues - you can read logs, trace data flows, and figure out what's broken without someone handing you the answer

• Strong communication skills - you can translate between engineering and business teams, explain technical concepts to non-technical stakeholders, and hold your own in a room with enterprise clients

• An ownership mindset - you care about customer outcomes, not just task completion; you'll follow a problem through to resolution even when it crosses team boundaries

• Experience in a client-facing technical role - Solutions Engineer, Implementation Engineer, FDE, or Technical Consultant at a SaaS or consulting company

• Exposure to conversational AI, voice platforms, workflow automation, or LLM-based systems

• Understanding of CRM, contact center, collections, or customer support systems - especially in BFSI

• Familiarity with prompt engineering or configuring AI agent behavior

• Cloud fundamentals (AWS or GCP) - enough to navigate infrastructure and deployment

• Ability to quickly learn new domains - BFSI has its own language and complexity, and we'll bring you up to speed, but a fast learning curve helps

• Work where AI meets the real world: This isn't research or prototyping - you'll deploy AI systems that handle millions of conversations for India's largest financial institutions. The feedback loop between what you build and how it performs is immediate.

• Fastest career development path available: FDEs at early-stage companies develop breadth that takes years to accumulate elsewhere. You'll build product intuition, customer empathy, technical depth, and business understanding simultaneously - the foundation for Solutions Architect, Product, or Customer Engineering leadership roles.

• Direct exposure to enterprise AI at scale: You'll work with 50+ enterprise clients across banking, insurance, and fintech - each with different systems, workflows, and constraints. This is a masterclass in building AI that works in production, not just in demos.

• Ownership and impact from day one: At our stage, every FDE directly influences customer outcomes and product direction. Your field insights will shape what we build next.

• Strong backing, proven team: Elevation Capital and Z47 are long-term partners invested in our vision. Our founders built and exited Cogno AI - they understand what it takes to build AI companies that earn enterprise trust.