Director – Generative AI

Job Description

Location: Hyderabad, Bangalore, & Pune

Experience: 12+ Years

Work Mode: Hybrid

Mandatory Skills: Generative AI, LLMs, AI/ML Strategy, NLP, Python, TensorFlow, PyTorch, Pandas, PySpark, MLOps, LLMOps, Cloud Platforms (Azure, AWS, GCP), API Architecture, Data Engineering, Leadership

Role Overview

As the Director Generative AI, you will be responsible for leading the strategy, development, and deployment of enterprise-grade Generative AI and Large Language Model (LLM) solutions. You will drive innovation across multiple business verticals by shaping the organization&aposs GenAI roadmap, managing high-performing data science teams, and fostering strong partnerships with stakeholders and technology partners.

You will provide both technical leadership and strategic direction to accelerate the adoption and scaling of Generative AI technologies, ensuring business impact, model reliability, and responsible AI practices.

Key Responsibilities

- Strategic Leadership: Define and drive the Generative AI strategy in alignment with organizational goals. Translate AI capabilities into enterprise-scale solutions across diverse use cases.
- Team Leadership & Mentoring: Build, mentor, and lead cross-functional AI/ML teams. Cultivate a high-performance culture with a focus on innovation, collaboration, and continuous learning.
- AI/ML Architecture Oversight: Architect and oversee the development of scalable, production-ready LLM and GenAI solutions, ensuring efficiency, accuracy, and maintainability.
- Lifecycle Management (MLOps/LLMOps): Establish best practices for managing the end-to-end lifecycle of GenAI models including experimentation, deployment, monitoring, governance, and retraining.
- Innovation in NLP & LLMs: Stay ahead of cutting-edge advancements in LLMs (e.g., GPT, LLaMA, Claude, Mistral) and drive their practical application in real-world enterprise scenarios.
- Data Strategy & Engineering: Guide data strategy and ensure robust pipelines using tools like Pandas, PySpark, and SQL, enabling clean, high-quality data for training models.
- Cloud & Infrastructure: Oversee the use of cloud-based ML platforms (e.g., Azure ML, AWS SageMaker, Databricks) for scalable development and deployment.
- AI Governance & Responsible AI: Ensure AI/ML initiatives follow responsible AI principles, including fairness, transparency, bias mitigation, and compliance.
- Stakeholder Collaboration: Engage with senior leadership, product, engineering, and operations teams to align AI solutions with business needs.
- Thought Leadership: Represent the organization in AI communities, conferences, and partnerships. Publish white papers, contribute to patents, and lead internal innovation forums.

Technical Expertise Required

- Programming & Development: Advanced proficiency in Python; experience developing robust ML pipelines and APIs.
- Generative AI & NLP: Deep expertise in GenAI techniques, including LLMs, prompt engineering, fine-tuning, and retrieval-augmented generation (RAG).
- Frameworks: Extensive hands-on experience with TensorFlow, PyTorch, Hugging Face Transformers.
- Cloud Platforms: Proven experience with cloud ML ecosystems Azure ML, AWS SageMaker, GCP Vertex AI, or Databricks.
- Lifecycle Ops: Strong knowledge of MLOps and LLMOps tools and practices.
- Data Engineering: Mastery of Pandas, PySpark, and SQL for data transformation and optimization.
- Visualization & Communication: Proficiency in Power BI, Tableau, or Matplotlib to effectively communicate insights.
- Statistical & Analytical Thinking: Strong foundation in statistical modeling, hypothesis testing, and performance optimization.

Qualifications

- Bachelors or Masters degree in Computer Science, Data Science, AI, or a related field (PhD preferred).
- 12+ years of experience in data science and machine learning, with at least 35 years in leadership roles.
- Proven track record of building and deploying Generative AI/LLM solutions at scale.
- Exceptional leadership, communication, and stakeholder management skills.

Skills: python,ai,mlops,ml,tensorflow,llms,llm,llmops,director level,pandas,api architecture,cloud platforms (azure, aws, gcp),data engineering,pyspark,generative ai,ai/ml strategy,machine learning,leadership,pytorch,neuro-linguistic programming (nlp),data science,nlp,artifical intelligence,architect designing

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