Director- Artifical Intelligence

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.
• 10–15 years of AI/ML experience, including 3–5 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.