Data Science Lead - GenAI

We are looking for a Specialist who will lead the design and deployment of enterprise-grade generative AI systems, driving innovation in LLM orchestration, multimodal architectures, and scalable AI/ML pipelines. Own the full lifecycle from research to production, ensuring alignment with business objectives and ethical  AI standards. This will be a hands-on individual contributor role as well as providing technical guidance to junior developers. 

Key Responsibilities 

1. Technical Leadership 

• Architect multi-LLM systems (e.g., Mixture-of-Experts, LLM routing) for cost-performance optimization. 

• Design GPU/TPU-optimized training pipelines (FSDP, DeepSpeed) for billion-parameter models. 

2. Cloud-Native AI Development 

• Build multi-cloud GenAI platforms (Azure OpenAI + GCP Vertex AI + AWS Bedrock) with unified MLOps. 

• Implement enterprise security: VPC peering, private model endpoints, and data residency compliance. 

3. Innovation & Strategy 

• Pioneer GenAI use cases: Agentic workflows, AI-driven synthetic data generation, real-time fine-tuning. 

• Establish AI governance frameworks: Model cards, drift monitoring, and red-teaming protocols. 

4. Cross-Functional Impact 

• Partner with leadership to define AI roadmaps and ROI metrics (e.g., $ saved via AI-driven automation). 

• Mentor junior engineers and evangelize GenAI best practices across the organization. 

Qualifications 

• Education: Bachelors/Masters in CS/AI or equivalent industry experience (5+ years in ML, 2+ in GenAI). 

• Technical Mastery: 

• Languages: Python. 

• Frameworks: Expert-level PyTorch, TensorFlow Extended (TFX), ONNX Runtime. 

• Cloud: Certified in Azure AI Engineer Expert and/or GCP Professional ML Engineer. 

• GenAI Expertise: 

• Shipped production GenAI systems (e.g., 10k+ QPS chatbots, code autocomplete at GitHub Copilot scale). 

• Advanced prompt/response engineering: Self-critique chains, LLM cascades, guardrail-driven generation. 

Must-Have Experience 

• Cloud AI experience: 

• Azure: Designed solutions with Azure OpenAI, MLOps Pipelines, and Cognitive Search. 

• GCP: Scaled Vertex AI LLM Evaluation, Gemini Multimodal, and TPU v5 Pods. 

• High-Impact Projects: 

• Automation projects to reduce significant $$ costs. 

• Built RAG  systems with hybrid search (vector + lexical) and dynamic data hydration. 

• Led AI compliance for regulated industries (healthcare, finance). 

Preferred Qualifications Additions 

• Certifications: 

• Azure: Microsoft Certified: Azure AI Engineer Associate. 

• GCP: Google Cloud Professional Machine Learning Engineer. 

• Experience with hybrid/multi-cloud GenAI deployments (e.g., training on GCP TPUs, serving via Azure endpoints).