About GreyLabs AI

GreyLabs AI builds enterprise-grade Voice AI systems for India’s BFSI ecosystem. Our technology powers high-volume, regulated use cases across collections, sales, servicing, compliance, and customer engagement.

We work with 40+ leading banks, NBFCs, fintechs, and insurance companies, solving complex, real-world problems where performance, reliability, auditability, and data security are non-negotiable. Our systems operate at scale, handling millions of interactions across diverse languages, accents, and customer contexts.

As a Series A-funded company, we are scaling rapidly - deepening our product capabilities, expanding our enterprise footprint, and building long-term technology moats in AI-first workflows.

At GreyLabs, ownership is real. We value clarity of thinking, bias for action, intellectual honesty, and high craft standards. Teams operate with autonomy, move fast, and are trusted to take ideas from concept to production.

If you’re excited about building meaningful technology in a high-impact, high-accountability environment, you’ll feel at home here.

The Role

This is a senior Individual Contributor role reporting to the VP of Engineering. The primary focus is the Voice AI Platform, with broader responsibility for engineering standards, architecture quality, and the technical development of senior engineers across the organisation. 

What You’ll Do

Voice AI Architecture

• Define and evolve the architecture for our real-time Voice AI pipeline - STT, LLM orchestration, TTS, and telephony - with low-latency requirements across Indian languages.

• Design agentic conversational workflows for BFSI use cases including collections, insurance sales, and customer service, with compliance, auditability, and contextual memory as first-class requirements.

• Own the Voice Analytics stack - speaker diarisation, sentiment analysis, compliance monitoring - designed to serve both real-time and batch analytical needs at scale.

Reliability, Infrastructure, and Cost

• Establish SLO and SLI frameworks, lead incident retrospectives, and build observability practices across the stack, including distributed tracing, structured logging, and actionable alerting.

• Own infrastructure cost efficiency - define unit economics per conversation and ensure that performance and cost are treated as co-equal design constraints.

• Raise the maturity of CI/CD pipelines, infrastructure-as-code, deployment safety practices, and multi-tenant reliability patterns for enterprise B2B workloads.

Technical Leadership

• Lead architecture reviews, produce design documents, and establish engineering standards across the organisation.

• Identify and build shared platform capabilities before individual product teams solve the same problems in parallel.

• Mentor senior engineers, contribute to IC hiring standards, and drive adoption of GenAI best practices - LLM observability, evaluation frameworks, prompt versioning, and model lifecycle management.

What We’re Looking For

Technical Experience

• 12+ years of software engineering, with at least 4-5 years in a principal or staff engineer capacity.

• Experience building real-time systems, streaming architectures, or voice and conversational AI in production environments.

• Working familiarity with the GenAI stack in production: LLMs, RAG pipelines, agentic frameworks, and vector databases.

Technical Judgement

• Able to make architectural decisions with incomplete information and document the reasoning in a way that stands up over time.

• Comfortable influencing technical direction across teams. Forms clear technical positions and revises them when presented with better evidence.

• Brings data and production evidence to trade-off conversations, and can make the case for technical investment to both engineers and business stakeholders.

Strong Signals

• Has made a significant architectural decision under uncertainty and can articulate the reasoning, the trade-offs accepted, and how it held up over time.

• Has owned a production incident affecting enterprise clients end-to-end - from triage through resolution and a post-mortem that produced lasting improvements.

• Has reduced infrastructure cost through architectural changes, with clear evidence of what changed and what it delivered.

• Has improved how a team delivers software - through CI/CD, observability, or deployment practices - with measurable outcomes.

• Has designed multi-tenant systems with deliberate isolation and blast radius controls, and can speak to those decisions in the context of enterprise security or compliance requirements.

• Has mentored senior engineers in a way that those engineers would describe as meaningful to their technical development.

Why GreyLabs AI

• A hard problem in a large market. Building low-latency, multilingual Voice AI for regulated financial institutions - across diverse Indian languages and under RBI and IRDAI compliance requirements - is technically complex and commercially consequential.

• Real scale, real engineering challenges. The reliability, cost, and infrastructure challenges here reflect actual production load.

• Scope to shape the technical direction. At our current stage, architectural decisions move quickly from design to production. Senior engineers have direct influence on how the platform is built and what it becomes.

• 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.

We’re committed to creating a fair, respectful, and inclusive workplace. From hiring to growth opportunities, our decisions are based on merit, skills, and potential, never on gender, identity, background, or any other personal characteristic. Talent has no labels here, and everyone is welcome to grow and thrive with us.