- Drive the establishment of governance frameworks, AI ethics standards, and responsible AI practices across the organization.

Technical Excellence & Delivery

- Architect and oversee the design, development, and deployment of scalable ML and GenAI systems from concept to production.

- Lead end-to-end project execution, including problem framing, model development, deployment, monitoring, and continuous improvement.

- Evaluate and introduce emerging AI technologies, ensuring the organization remains at the forefront of innovation.

- Define best practices for MLOps, model governance, and lifecycle management, ensuring reliability and scalability of deployed models.

Team Leadership & Mentorship

- Build, manage, and mentor a high-performing team of data scientists, ML engineers, and AI researchers.

- Foster a culture of continuous learning, experimentation, and innovation within the data science organization.

- Provide technical leadership and guidance, ensuring that teams adhere to best practices in data management, modeling, and deployment.

- Drive career development, performance management, and succession planning for team members.

Cross-Functional & Organizational Impact

- Collaborate with Product, Engineering, and Business leaders to define data-driven strategies and integrate AI into products and operations.

- Act as a trusted advisor to senior stakeholders, translating complex technical concepts into actionable business insights.

- Champion the data-driven decision-making culture across departments by promoting data literacy and analytics best practices.

- Manage budgeting, resource allocation, and vendor partnerships related to AI and data science initiatives.

Essential Qualifications

- 10-12 years of experience in Data Science, Machine Learning, or AI, including 5+ years in a senior or technical leadership capacity.

- Proven experience with Large Language Models (LLMs) (OpenAI, Anthropic, LLaMA) including prompt engineering, fine-tuning, and embedding-based retrieval systems.

- Expertise in Python and its core libraries: NumPy, Pandas, scikit-learn, PyTorch/TensorFlow, and Hugging Face Transformers.

- Demonstrated success in delivering end-to-end Generative AI or advanced NLP solutions (e.g., conversational AI, summarization, custom NER, or document Q&A systems) into production.

- Deep understanding of AI deployment tools such as Docker, Kubernetes, Airflow, and API frameworks (Flask, FastAPI).

- Strong experience with data pipeline architecture, MLOps, and AI model governance frameworks.

Preferred Qualifications

- Masters or Ph.D. in Computer Science, Data Science, Statistics, or a related quantitative field.

- Experience in cloud platforms (AWS, GCP, or Azure) for model training, deployment, and scaling.

- Familiarity with Retrieval-Augmented Generation (RAG), vector databases (e.g., Pinecone, FAISS, Weaviate), and multi-modal AI systems.

- Proven ability to influence senior executives and drive AI adoption across diverse business functions

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Posted By

Lithinraj

Manager - TA at TALWORX SOLUTIONS PRIVATE LIMITED