Associate Director

Description : Key Responsibilities Leadership & Vision : - Define and execute the organizations AI and Data Science strategy, ensuring alignment with business objectives and technology roadmaps.- Lead the strategic adoption of Generative AI and advanced ML to create innovative, data-driven products and services.- Partner with executive leadership to identify new business opportunities enabled by AI and analytics.- 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 (ref:hirist.tech)