Job Description & How to Apply Below
As the Gen AI Lead, you will spearhead our organization’s 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 Lang Chain, Auto Gen, 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).Must know MCP Server integration.
- Programming:
Mastery of Python and frameworks like PyTorch or Tensor Flow.
- 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.
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