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AI Tech Lead (Hybrid, Bengaluru)

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Role: Lead AI Infra & Multi-Agent Orchestration
Location: Hybrid – Bengaluru (candidate must be based in Bengaluru)
Type: Contractual | Immediate Joining
Experience: 7+ years in Software Engineering | 3+ years in ML/LLM deployment.
Salary: 24 LPA

Role Overview

• Define the tech architecture for multi-agent AI workflows.
• Lead development of Agent-to-Agent Handoff, ensuring context and content transfer between agents (e.g., Content → Design → Video).
• Work with self-hosted LLMs and open-source frameworks to build secure, private AI pipelines.
• Ensure no external API dependency for sensitive data; architect infra where data remains within our environment.
• Make strategic infra decisions (GPU clusters, vector DBs, orchestration tools) to balance speed, security, and cost.
• Mentor a team of engineers, establish best practices for local model deployment, optimization, and scaling.
• Collaborate cross-functionally with Product and Design teams to deliver features faster and more efficiently.

Key Responsibilities:

• Architect and own the end-to-end stack (LLM hosting, backend, infra, orchestration).
• Build and optimize multi-agent orchestration with contextual memory and handoff.
• Deployment and fine-tuning LLMs
• Implement secure data pipelines with role-based access, encryption, and monitoring.
• Drive infra efficiency — choosing between cloud GPU clusters, on-prem setups, and hybrid infra.
• Ensure compliance with GDPR, and privacy standards
• Oversee DevOps practices (CI/CD, logging, monitoring, auto-scaling).
• Continuously evaluate emerging OSS models and frameworks to improve cost/performance.

Qualifications:

• 7+ years in software engineering/AI systems, 3+ years in ML/LLM deployment.
• Hands-on with LLM hosting (Ollama, HuggingFace, LangChain, vLLM, LoRA fine-tuning).
• Experience deploying GenAI beyond text (diffusion models for image, video generation).
• Deep knowledge of cloud GPU infra (RunCloud, AWS/GCP/Azure) + Kubernetes/Docker.
• Strong backend skills (Python, FastAPI/NodeJS, microservices, event-driven systems).
• Proven track record of leading engineering teams and delivering production AI products.
• Strong understanding of vector databases (Pinecone, Weaviate, Milvus) and retrieval pipelines (RAG).
• Excellent communication to bridge product, design, and tech teams.

Nice to Have:

• Experience in Agentic AI or multi-agent orchestration systems.
• Prior work on marketing-tech or SaaS platforms.
• Knowledge of GPU optimization techniques (quantization, batching, caching).
• Exposure to privacy-first AI design where no data leaves the system.