Assistant Vice President - Data Science & Artificial Intelligence

Key Responsibilities :

• Lead and manage the AI / ML team in designing, developing, and deploying advanced machine learning and LLM-based solutions.
• Develop RAG systems and other NLP / AI applications to improve business intelligence and decision-making.
• Oversee end-to-end MLOps processes, including model development, versioning, deployment, monitoring, and scaling.
• Ensure efficient integration of AI solutions into business applications and workflows.
• Collaborate with product, engineering, and business teams to align AI initiatives with organizational objectives.
• Evaluate emerging AI / ML technologies, frameworks, and tools (e.g., Hugging Face, TensorFlow, PyTorch) to continuously improve solutions.
• Establish best practices for data handling, model training, evaluation, and ethical AI usage.
• Provide technical mentorship and strategic guidance to junior data scientists and AI Skills :
• Artificial Intelligence & Machine Learning (AI / ML)
• Large Language Models (LLMs) & RAG systems
• NLP & Predictive Modeling
• MLOps & Model Lifecycle Management
• Cross-functional Leadership & Strategic Skills :
• Programming & Frameworks : Python, TensorFlow, PyTorch, Scikit-learn
• NLP & LLM Tools : Hugging Face Transformers, RAG frameworks
• Cloud Platforms : GCP, AWS, Azure
• Workflow & MLOps Tools : MLflow, Airflow, Docker, Kubernetes
• Data Handling : SQL, Pandas, NumPy, BigQuery, or & Experience :
• Bachelors or Masters degree in Computer Science, Data Science, AI, or related field (PhD preferred for advanced research roles).
• 5+ years of experience in AI / ML with hands-on expertise in LLMs, RAG, and MLOps.
• Proven experience in cloud-based AI / ML deployments and managing high-performing technical / Success Metrics :
• Accuracy, F1 score, and other performance metrics for deployed AI / ML models.
• Efficiency and scalability of MLOps pipelines.
• Business impact from AI / ML initiatives (e.g., automation, revenue growth, process improvement).
• Number of successful AI / ML projects delivered within timelines.
• Adoption and integration rate of AI solutions across business units

(ref : hirist.tech)