Lead AI Architect - Infrastructure & ModelOps

About the Role

Lead AI Architect

foundational role

Salary

30 - 45 LPA with additional benefits.

Key Responsibilities

• Define and own the

  AI/ML architectural roadmap

  , aligning with product vision and technical goals.
• Architect and oversee implementation of

  RAG-based solutions

  ,

  LLM/SLM fine-tuning pipelines

  , and

  multi-agent orchestration

  .
• Lead design of model training and inference pipelines ensuring scalability, modularity, and observability.
• Evaluate and select open-source and proprietary foundation models for fine-tuning, instruction tuning, and domain adaptation.
• Guide integration of vector databases, semantic search, and prompt orchestration frameworks (LangChain, LlamaIndex, etc.).
• Ensure best practices in

  model deployment, versioning, monitoring

  , and performance optimization (GPU utilization, memory efficiency, etc.).
• Collaborate with Engineering, DevOps, Product, and Data Science teams to bring AI features to production.
• Mentor mid-level engineers and interns; contribute to

  technical leadership

  and

  code quality

  .
• Maintain awareness of latest research, model capabilities, and trends in AI.

Required Skills & Qualifications

• 6+ years of hands-on experience in AI/ML architecture and model deployment.
• Expert-level knowledge of Python and libraries such as PyTorch, Hugging Face Transformers, scikit-learn, and FastAPI.
• Deep understanding of LLMs/SLMs, embedding models, tokenization strategies, fine-tuning, quantization, and LoRA/QLoRA.
• Proven experience with

  Retrieval-Augmented Generation (RAG)

  pipelines and vector DBs like FAISS, Pinecone, or Weaviate.
• Strong grasp of system design, distributed training, MLOps, and scalable cloud-based infrastructure (AWS/GCP/Azure).
• Experience with containerization (Docker), orchestration (Kubernetes), and experiment tracking (MLFlow, W&B).
• Experience in building secure and performant

  REST APIs

  , deploying and monitoring AI services in production.

Nice to Have

• Exposure to multi-agent frameworks, task planners, or LangGraph.
• Experience leading AI platform teams or architecting enterprise-scale ML platforms.
• Familiarity with Data Governance, Responsible AI, and model compliance requirements.
• Published papers, open-source contributions, or patents in the AI/ML domain.

Why Join Us

• Be at the forefront of innovation in AI and language intelligence.
• Influence strategic technical decisions and drive company-wide AI architecture.
• Lead a growing AI team in a high-impact, fast-paced environment.
• Competitive compensation, equity options, and leadership opportunity.