Data Science Lead - GenAI

Overview

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.

Responsibilities

Technical Leadership

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

Cloud-Native AI Development

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

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.

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.

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).

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