Artificial Intelligence Architect (14-16 yrs)

We are looking for a hands-on AI Architect with deep expertise in Generative AI, Machine Learning, and a strong architectural mindset to drive innovative solutions in real-world business scenarios. Experience in Healthcare domains and deploying solutions to production environments on AWS or Azure will be highly valued.

This is a senior-level opportunity to play a critical role in shaping and delivering cutting-edge AI strategies and architectures for scalable, intelligent solutions.

Key Responsibilities :

- Architect end-to-end AI/ML and Generative AI solutions tailored to business needs, with a focus on scalability, performance, and real-time processing.

- Lead the design and implementation of AI systems using AWS or Azure cloud ecosystems.

- Drive innovation by exploring and integrating new AI technologies and tools, especially within the Generative AI landscape (e.g., LLMs, transformers, diffusion models).

- Collaborate with cross-functional teams including data engineers, DevOps, product managers, and domain experts to develop AI-powered applications.

- Oversee the transition of AI models and solutions from prototype to production-grade environments.

- Define and enforce best practices for model development, evaluation, versioning, monitoring, and retraining.

- Stay updated with the latest advancements in AI/ML and contribute to knowledge sharing within the team.

Required Skills & Experience :

- 14+ years of overall experience with a strong foundation in software engineering, AI/ML system design, and cloud-native architecture.

- Hands-on experience with Generative AI, LLMs, NLP, and deep learning frameworks (e.g., PyTorch, TensorFlow, Hugging Face).

- Proven track record of building and deploying AI/ML solutions to production environments using AWS and/or Azure.

- Proficient in Python and ML lifecycle tools (e.g., MLflow, SageMaker, Azure ML, Kubeflow).

- Experience with MLOps, containerization (Docker), and orchestration (Kubernetes) is desirable.

- Strong understanding of data pipelines, model tuning, versioning, and performance monitoring.

- Exposure to Healthcare or Life Sciences domain is a plus.

Nice to Have :

- Experience with Responsible AI frameworks and model explainability tools.

- Familiarity with regulations and compliance relevant to AI in healthcare (e.g., HIPAA, HL7, FHIR).

- Experience with open-source LLMs or fine-tuning models for enterprise applications.