AWS AI Architect

1. Defining and executing the AI/ML strategy, using AWS Services Bedrock, Sagemaker, AgenticCore etc
2. Driving R&D in areas like NLP, deep learning, generative AI, and multimodal applications,

Staying at the forefront of emerging technologies.

1. Challenge conventional thinking, prioritize risks, align stakeholders, and deliver innovative solutions for complex scientific and operational challenges.

Technical Skills/Scope

1. architecting and deploying scalable models (including LLMs and transformer-based architectures),
2. implementing MLOps best practices for CI/CD, automated retraining, and lifecycle management,
3. developing advanced solutions like retrieval-augmented generation and fine-tuning techniques.
4. optimize pipelines for distributed computing, ensure compliance with data governance, and build production-ready models across NLP, computer vision, and multimodal domains using frameworks such as PyTorch, TensorFlow, Scikit-learn, and Hugging Face.
5. fine-tune and prompt-engineer LLMs for IBU-specific pharma use cases and develop scalable APIs and microservices for model serving.
6. implementing CI/CD pipelines using tools like GitHub Actions, MLflow, Kubeflow, SageMaker, and VertexAI, and establishing frameworks for governance, reproducibility, and drift monitoring.
7. operate cloud-native ML environments across AWS, GCP, and Azure, use Infrastructure-as-Code tools for scalable deployments, and containerize workloads with Docker and Kubernetes.
8. Ideal technical skills include deep expertise in ML algorithms and statistical modeling, proficiency in NLP, computer vision, LLM-based systems, and advanced Python skills.
9. Familiarity with vector databases, cloud-native data warehouses, MLOps tools, feature stores, and event-driven architectures is highly valued. Preferred skills are experience with multi-agent frameworks, prompt optimization, enterprise-grade AI security, and prior leadership in AI Centers of Excellence.