Lead Data Scientist

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.

1. 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.

1. 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.

1. 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 RAGsystems 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).