Lead Data Scientist - Gen AI & Traditional AI

Job Title: Lead Data Scientist Gen AI & Traditional AI

Role Summary

We are looking for a Lead Data Scientist with deep, hands-on experience across Traditional AI/ML and Generative AI (GenAI). This role will lead end-to-end AI experimentation and delivery across multiple parallel initiatives, guide and mentor senior data scientists/engineers, and actively participate in client-facing activities (workshops, demos, solutioning). You will also contribute to delivery governancedefining scope, estimating effort, building sprint/resource plans, and ensuring execution quality.

Key Responsibilities

Technical Leadership & Delivery

• Lead AI/ML solution design and implementation across multiple projects running in parallel.
• Break down complex AI use cases into well-defined tasks and milestones; guide Senior Data Scientists and cross-functional teams to successful delivery.
• Own experimentation strategy: dataset readiness, feature engineering, model selection, tuning, evaluation, and iteration loops.
• Ensure production-grade readiness: performance, reliability, scalability, cost- efficiency, and monitoring/observability requirements.

GenAI / LLM Expertise

• Drive development of GenAI features using proprietary and open-source LLM ecosystems.
• Demonstrate strong understanding of GenAI architectures and underlying mathematics (e.g., transformer fundamentals, attention mechanisms, optimization, embeddings, decoding strategies, fine-tuning approaches).
• Build and optimize RAG pipelines (chunking strategies, embeddings, retrieval, reranking, grounding, evaluation).
• Design and implement agentic workflows using modern agent frameworks and tool integrations (planning, tool-use, multi-step execution, safety/guardrails).
• Establish evaluation frameworks for GenAI quality (hallucination risk, faithfulness, relevance, latency, cost).

Traditional AI / ML Expertise

• Lead classical ML initiatives including supervised/unsupervised learning, time series, NLP (non-LLM), recommendation, anomaly detection, etc., as applicable.
• Define end-to-end ML workflows: data pipelines, training, validation, deployment patterns, and performance tracking.

Stakeholder & Client Engagement

• Participate in client discussions: requirement discovery, solution walkthroughs, technical deep-dives, and demos.
• Translate business needs into implementable AI deliverables with clear success criteria.
• Provide regular status updates, risks, and mitigation plans to stakeholders.

Planning, Governance & Execution Management

• Own/drive SOW scope inputs and contribute to task-level resource planning and estimations.
• Create sprint plans, manage execution priorities, and coordinate dependencies across AI, engineering, and DevOps teams.
• Define best practices, reusable assets, and internal standards across experimentation and delivery.

Required Qualifications

• 10+ years of hands-on experience in AI/ML and data science experimentation with proven delivery outcomes.
• Demonstrated experience leading teams and guiding implementation of AI features end-to-end.
• Strong experience working on multiple projects in parallel and handling competing priorities.
• Strong understanding of both proprietary and open-source model ecosystems and trade-offs (cost, privacy, latency, deployment constraints).
• Hands-on experience with RAG and agentic frameworks (design + implementation).
• Ability to structure work into clear tasks, guide senior team members, and ensure high-quality execution.
• Strong communication skills for client interactions, demos, and stakeholder alignment.

Preferred / Nice-to-Have

• Experience building enterprise-grade AI systems (security, governance, auditability, data privacy).
• Experience with LLM fine-tuning techniques (LoRA/QLoRA, instruction tuning, domain adaptation) and evaluation tooling.
• Experience with MLOps/LLMOps patterns (CI/CD, model monitoring, prompt/version management, A/B testing).
• Exposure to multi-cloud or hybrid deployments (AWS/Azure/on-prem).

Key Competencies

• Technical depth in both GenAI and Traditional ML
• Ownership mindset and delivery rigor
• Strong problem decomposition and team guidance
• High-quality stakeholder management and client communication
• Ability to balance experimentation speed with production readiness

Reporting & Collaboration

• Works closely with: AI Engineers, Data Engineers, MLOps/DevOps, Full-stack/Backend teams, Product/Pr…