Director- Artificial Intelligence

Job Description Director Artificial Intelligence Work Location: Hyderabad or Chennai (open to Mumbai, Pune) Work Arrangement: Requires a minimum of 9 working days per month from the Chennai office, with the remaining days flexible to be worked from any of the other listed locations. About The Role We are seeking a technically accomplished leader to serve as Director Artificial Intelligence within Acentra Health's AI Center of Excellence (COE). This role will provide strategic input into enterprise AI initiatives, working as a core member of the COE while partnering with business lines across the organization. The Director will bring deep expertise across machine learning, generative AI, and agentic AI, with a focus on architectural leadership, model selection, model tuning, and ML Ops. Key Responsibilities AI Strategy & Vision - Contribute to the definition of Acentra Health's AI strategy as a member of the AI Center of Excellence. - Partner with business lines to identify and prioritize high-value AI opportunities across ML, GenAI, and agents. - Team Leadership - Lead and mentor AI/ML engineers, Data/BI engineers, and full stack engineers within the COE. - Establish best practices for coding, experimentation, and technical excellence. AI Solution Development Direct the design, tuning, and deployment of advanced AI/ML models, including: - LLMs & Multimodal Models: Frontier and open-source models, instruction-tuned models, conversational AI, and retrieval-augmented generation (RAG). - AI Agents: Agent-based systems leveraging OpenAI's Agents SDK and Model Context Protocol (MCP) to support orchestration, task automation, and human-in-the-loop collaboration. - Predictive Modeling & Recommendation Systems. MLOps & Productionization - Implement and support ML pipelines for preprocessing, feature engineering, model training, deployment, and monitoring. - Ensure reproducibility and scalability using cloud-native services and frameworks (e.g., AgentCore with AWS). - Manage CI/CD workflows leveraging Docker, Kubernetes, and AWS-native services. Platform & Infrastructure - Collaborate with data engineering and IT to design scalable AI infrastructure. - Utilize AWS (SageMaker, Bedrock, AgentCore), with additional experience in Azure ML and GCP as beneficial. - Optimize models for performance, latency, and cost efficiency. Innovation & Applied Research - Stay at the forefront of emerging technologies such as generative AI, agent frameworks, and reinforcement learning. - Foster a culture of applied innovation, continuous learning, and responsible AI adoption. Expanded Technical Responsibilities - Collaborate with full stack and UI teams to seamlessly integrate AI features into products. - Provide architectural guidance for AI enablement across applications and workflows. - Define and implement robust testing frameworks for AI models, ensuring accuracy, fairness, and reliability. - Partner with data engineers to design and maintain scalable data pipelines (Airflow, Spark, Kafka, AWS Glue, Azure Data Factory). - Align AI initiatives with product roadmaps and monitor post-deployment performance. Candidate Profile Education & Experience - Bachelor's or Master's degree in AI, Computer Science, Data Science, or related fields. - 1015 years of AI/ML experience, including 35 years in leadership roles. - Proven success in deploying AI solutions at scale in production environments. Technical Expertise - Programming: Python (NumPy, Pandas, SciPy, scikit-learn). - AI/ML Frameworks: TensorFlow, PyTorch, Keras, Hugging Face. - Agents & GenAI: OpenAI Agents SDK, Model Context Protocol (MCP), RAG pipelines, multimodal models. - MLOps Tools: AgentCore with AWS, SageMaker, Azure ML. - Data Pipelines: Apache Airflow, Spark, Kafka, AWS Glue, Azure Data Factory. - DevOps & CI/CD: GitHub Actions, Jenkins, Docker, Kubernetes. - Cloud Ecosystems: AWS (priority), with Azure and GCP experience a plus. - Optimization: Quantization, pruning, distributed training. Soft Skills - Excellent communication to translate AI value for technical and executive stakeholders. - Strong leadership built on collaboration, accountability, and innovation. - Passion for ethical AI, responsible adoption, and scaling.