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NStarX Logo NStarX · IT - Software Development

Lead Data Scientist – GenAI & Traditional AI

India, Hyderabad · Job Posted April 16, 2026
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Job Description

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 governance—defining scope, estimating effort, building sprint/resource plans, and ensuring execution quality.

Job Responsibility

  • 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
  • Drive development of GenAI features using proprietary and open-source LLM ecosystems
  • 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)
  • 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
  • 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
  • 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

Requirements

  • 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

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)
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