Role Overview
We're looking for an Engineering Manager to lead and scale our engineering team as we move from research breakthroughs to production-grade AI products. You'll own delivery across our product lines, build a high-performance engineering culture, and act as the bridge between our research scientists, product goals, and customer commitments.
Role & Responsibilities
Team Leadership & Growth
- Hire, mentor, and retain a team of 815 engineers across ML, backend, and infrastructure
- Conduct regular 1:1s, performance reviews, and career development planning
- Build an engineering culture of ownership, velocity, and technical rigor suited to a fast-moving AI lab
Technical Delivery & Execution
- Own end-to-end delivery of AI products: voice agents, LLM fine-tuning pipelines, TTS/ASR services, and vision model deployments
- Drive sprint planning, roadmap execution, and cross-team dependencies; unblock teams and manage trade-offs between speed and quality
- Ensure production systems meet latency (sub-second for voice), reliability, and cost-efficiency targets
- Oversee MLOps practices: model deployment, monitoring, versioning, and continuous improvement pipelines
Architecture & Technical Direction
- Guide architectural decisions for scalable, low-latency AI inference systems (including distributed/community GPU infrastructure)
- Review designs and critical code paths; set engineering standards, documentation practices, and security/compliance baselines
- Partner with research teams to productionize models bridging the gap between research prototypes and reliable customer-facing systems
Must-haves
- 8+ years of software engineering experience, with 2+ years managing engineering teams
- Strong hands-on background in backend systems, distributed systems, or ML engineering (Python; Go/Node.js a plus)
- Direct experience shipping ML/AI products to production LLM applications, speech systems, or computer vision
- Familiarity with MLOps tooling, GPU infrastructure, model serving frameworks (vLLM, Triton, TorchServe, or similar), and cloud platforms (AWS/GCP/Azure)
- Proven track record of hiring and scaling teams in a startup or high-growth environment
- Excellent communication able to work with researchers, customers, and founders alike
Nice-to-haves
- Experience with real-time/low-latency systems (voice, streaming, telephony integrations)
- Exposure to LLM fine-tuning, RAG pipelines, or speech model training (TTS/ASR)
- Prior experience at an AI-first startup or research lab
- Experience working with Indian language datasets/models or building for the Indian market
- Contributions to open-source AI/ML projects