Description :

Job Description : Principal AI/ML Architect GenAI & Agentic AI.

Role Overview :

We are looking for a Principal AI/ML Architect with deep expertise in Traditional Machine Learning, Generative AI, and Agentic AI architectures to lead the design and delivery of enterprise-scale AI solutions.

This role requires strong hands-on capability along with architectural ownership, stakeholder engagement, and technical leadership across the AI lifecycle.

Experience :

- 14 to 24 years of overall IT experience.

- 10+ years of hands-on experience in Data Science / AI / ML.

- Prior experience in Architect / Principal / Lead roles is mandatory.

Key Responsibilities :

- Architect, design, and lead end-to-end AI/ML and GenAI solutions from ideation to production.

- Define enterprise-grade architectures for :

- Traditional ML & Deep Learning systems.

- Generative AI (LLMs) use cases.

- RAG, Self-RAG, and advanced retrieval pipelines.

- Agentic AI / Multi-Agent orchestration frameworks.

- Own solution design covering :

- Data ingestion, feature engineering, model training.

- Deployment, monitoring, retraining, and drift detection.

- Design scalable AI services using microservices and event-driven architectures.

- Lead MLOps pipelines for CI/CD, model versioning, monitoring, and governance.

- Work closely with business stakeholders to translate requirements into AI-driven solutions.

- Mentor and guide teams of data scientists, ML engineers, and MLOps engineers.

- Drive POCs, innovation initiatives, and adoption of state-of-the-art AI techniques.

- Ensure solutions meet performance, security, compliance, and explainability standards.

Technical Skills :

Mandatory :

AI / ML / GenAI :

- Machine Learning, Deep Learning, NLP, Computer Vision.

- Generative AI using GPT, LLaMA, Mistral, Claude or equivalent LLMs.

- RAG, Self-RAG, embeddings, vector databases.

- Agentic AI / Multi-Agent frameworks (LangChain, LangGraph, AutoGen, CrewAI or similar).

- Statistical modeling, feature engineering, model evaluation.

Programming & Frameworks :

- Python (strong hands-on).

- TensorFlow / PyTorch / Keras.

- REST APIs & microservices.

Cloud & MLOps :

- AWS / GCP / Azure (any one mandatory, multi-cloud preferred).

- Docker, Kubernetes.

- CI/CD pipelines, model monitoring, logging, drift detection.

- Tools like SageMaker, Bedrock, Airflow, MLflow (or equivalents).

Data & Platforms :

- SQL & NoSQL databases.

- Vector databases & knowledge graphs.

- Kafka / SQS or similar event-driven systems.

Nice to Have :

- Experience in Banking, Financial Services, Investment Banking, Healthcare, or Automation.

- Experience handling large-scale enterprise AI deployments.

- Exposure to Explainable AI, Responsible AI, AI governance.

- Strong understanding of Agile delivery models.

Soft Skills :

- Strong communication and stakeholder management.

- Ability to translate business problems into AI solutions.

- Technical leadership and mentoring capability.

- Strategic thinking with hands-on execution mindset.