GenAI Lead

Role Summary

As the Gen AI Lead, you will spearhead our organizations Generative AI strategy, moving beyond simple API integrations to build sophisticated, production-grade AI systems. You will lead a cross-functional team of engineers and data scientists to architect Agentic AI frameworks, optimize RAG (Retrieval-Augmented Generation) pipelines, and ensure our AI solutions are ethical, scalable, and secure.

Key Responsibilities

• Architecture & Design: Lead the design of multi-agent systems using frameworks like LangChain , AutoGen , or Semantic Kernel .
• Model Engineering: Oversee the fine-tuning of Large Language Models (LLMs) and Small Language Models (SLMs) using techniques like LoRA/QLoRA for domain-specific accuracy.
• LLMOps & Scalability: Establish robust deployment pipelines (CI/CD for AI), ensuring model monitoring, versioning, and cost-efficient scaling in the cloud (AWS/Azure/GCP).
• Governance & Ethics: Implement "Responsible AI" guardrails to mitigate hallucinations, bias, and data leakage, ensuring compliance with emerging AI regulations.
• Cross-Functional Leadership: Act as the primary technical SME for executive stakeholders, translating "AI hype" into measurable ROI and business outcomes.

Required Technical Skills

• Core AI: Deep expertise in Transformer architectures, Diffusion models, and Vector Databases (e.g., Pinecone, Chroma, Milvus).
• Programming: Mastery of Python and frameworks like PyTorch or TensorFlow .
• Agentic Frameworks: Hands-on experience building autonomous agents and complex multi-step workflows.
• Data & RAG: Expert-level knowledge of chunking strategies, embedding models, and hybrid search techniques.
• Cloud & Infrastructure: Proficiency with Kubernetes , Docker, and AI-specific cloud services (e.g., Azure AI Foundry, AWS Bedrock, Google Vertex AI).

Experience & Qualifications

• Experience: 8+ years in AI/ML, with at least 2 years specifically focused on Generative AI initiatives.
• Leadership: Proven track record of managing high-performing technical teams and delivering complex software products at scale.
• Communication: Ability to explain "black box" AI concepts to non-technical partners with clarity and authority.

Nice-to-Haves

• Contributions to open-source GenAI projects or published research in the field.
• Experience with Multi-modal AI (Text + Image/Video/Audio).
• Certifications such as AWS Certified Machine Learning Specialty or Microsoft Azure AI Engineer Associate .