Top Tech Jobs & Startup Jobs

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.

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.

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.

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.

Own the product roadmap for Chanakya atoms, translate field deployments into precise specs, run structured enterprise discovery, prioritise features, define product metrics, write PRDs and decision logs, coordinate engineering sprints, and manage stakeholder communication to deliver deployable AI components.

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.

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.

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.

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 and optimize scalable Next.js and TypeScript web applications, create reusable components, integrate RESTful APIs, ensure performance, accessibility, and cross-browser compatibility, maintain quality through testing and code reviews, and collaborate with designers, backend engineers, and AI teams using monitoring, analytics, and A/B testing.