Jobs at Sarvam

Senior client-facing role owning GTM, pipeline, and account strategy for defence and strategic clients. Build decision-level relationships, translate technical AI capability into proposals, lead RFI/RFP and PoC scoping, negotiate commercially, collaborate with engineers and product, manage account health, and surface requirements and competitor/policy shifts.

Design and implement production-grade backend systems for on-prem and constrained deployments: MCP servers, ingestion pipelines, agentic backends, APIs, containerised tooling, and operational/debugging support for deployments.

Build distributed microservices and task-graph execution engines for LLM-driven workflows. Integrate multiple LLM providers and tools, implement gRPC/REST APIs, document ingestion and embedding pipelines, vector search knowledge bases, safe code-execution sandboxes, and contribute to MCP server implementations while maintaining containerized infrastructure and observability.

Own financial reporting, controllership, compliance, and accounting operations. Manage month/quarter/year close, audits, tax filings, AP/AR/payroll, build management reporting, automate finance processes, and support fundraising and international consolidation.

Own the full enterprise sales cycle: prospect, engage C-level stakeholders, conduct discovery, negotiate and close deals. Collaborate with BDRs/SDRs, marketing, and product, maintain CRM discipline, forecast pipeline, and ensure smooth handoffs to customer success to drive acquisition and long-term partnerships.

Lead go-to-market and positioning for enterprise AI products. Build vertical narratives, sales enablement content, case studies, demos, and ROI benchmarks. Partner with product and sales, gather field insights, and support long-cycle enterprise deals in financial services, public sector, and healthcare.

Design, build, and maintain ML systems for a multilingual dubbing platform. Develop automated validation, monitoring, and evaluation workflows. Optimize inference pipelines for performance, cost, and reliability, run experiments, and collaborate with product and engineering to productionize models.

Build and deploy vision-language models across the full lifecycle: data pipelines, training/fine-tuning on GPU clusters, evaluation and benchmarks, inference optimization, production integrations, and client-facing solutions for document processing and visual search.

Work with product and customers to design, build, deploy, monitor, and iterate production-grade agentic AI systems across channels. Implement multi-agent architectures, data pipelines, integrations, and evaluation metrics; own client engagements from kickoff to go-live.

Own end-to-end deployment of Sarvam's full AI stack in on-prem, air-gapped, and classified client environments. Act as technical SPOC from scoping to steady-state, diagnose integration and inference failures, manage deployment pipelines and model serving, drive adoption via training/documentation, and feed field learnings back into product.

Conduct research across vision-language model lifecycles: architecture, training (pretraining, SFT, RLHF, DPO), data strategies, evaluation and benchmarks for Indic multimodal tasks; diagnose failures, improve robustness and interpretability, prototype at scale, and engage with open-source and research community.

Operate and own model lifecycle for defence and strategic deployments: design serving infra, CI/CD for model updates, monitoring/observability, evaluation/A-B testing, containerised serving for edge and air-gapped environments, collaborate on eval pipelines, create runbooks, and lead incident response for production model failures.

Lead an AI product area end-to-end: speak with users, write specs, ship weekly, instrument funnels, and measure outcomes. Work on prompt design, model selection, latency/failure modes, eval pipelines, and occasionally write code, prompts, and copy. Collaborate directly with engineering and design to iterate rapidly and deliver production-ready AI features across consumer, enterprise, and edge use cases.

Lead outbound prospecting to generate qualified enterprise SaaS leads via cold calls, email and LinkedIn. Conduct discovery calls, qualify opportunities, collaborate with account executives and marketing, and maintain CRM records to accelerate pipeline growth.

Build and scale high-throughput, low-latency Vision APIs and platform infrastructure. Develop Python/FastAPI services, implement observability, security, CI/CD, container orchestration, async task processing, and real-time HTTP/WebSocket communication across cloud environments.

Own frontend quality across web, streaming, and AI interaction layers. Define and implement testing strategy, build automation systems (component/integration/visual regression), use AI to accelerate testing, debug browsers, and partner with frontend engineers to prevent incidents and block releases when quality thresholds arent met.

Lead backend engineering for Document Intelligence and Vision APIs: architect for low latency and high throughput, scale services, build observability and security foundations, manage infra and deployments, improve developer experience, mentor engineers, and define CI/CD and inference-serving strategies.

Drive end-to-end enterprise sales for Sarvam's conversational AI solutions to large BFSI organisations. Build trusted relationships with decision-makers, understand client pain points, coordinate internal teams to structure solutions, manage complex procurement and multi-stakeholder deals, ensure smooth deployment with customer success, and monitor market trends and competitive landscape.

Drive outbound prospecting and lead qualification via cold calls, email, and LinkedIn; engage enterprise decision-makers, conduct discovery calls, maintain CRM records, and collaborate with AEs and marketing to grow pipeline.

Build production-grade, performant frontend interfaces for AI-driven enterprise use cases (geospatial, simulation, NLP layers, dashboards). Own component architecture, deployment of reusable product atoms in offline-first, low-bandwidth client environments. Collaborate with backend, PM, and deployment teams; contribute to shared frontend infrastructure, participate in field testing and user feedback loops.