Data Science Lead

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).

Preferred candidate profile

• 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).