:

Key Responsibilities :

AI/ML Strategy &

- Architecture :
- Define and execute AI/ML architecture strategies aligned with business objectives.
- Design scalable, secure, and high-performance AI/ML solutions across cloud and on-premises environments.
- Lead the architecture of predictive analytics, Generative AI, NLP, Computer Vision, Recommendation Systems, and MLOps platforms.
- Evaluate emerging AI technologies and recommend adoption strategies.
- Establish AI governance, model lifecycle management, and responsible AI practices.

Solution Design &

- Development :
- Architect end-to-end machine learning pipelines including data ingestion, feature engineering, model training, deployment, and monitoring.
- Design and implement enterprise AI platforms using Azure AI, AWS AI/ML, Google Cloud AI, OpenAI, Anthropic, or similar ecosystems.
- Lead model selection, experimentation, optimization, and performance tuning.
- Define integration patterns between AI services and enterprise applications.

Leadership &

- Stakeholder Management:
- Lead and mentor AI Engineers, Data Scientists, ML Engineers, and Data Engineers.
- Collaborate with business stakeholders to identify AI use cases and translate requirements into technical solutions.
- Drive technical discussions, architecture reviews, and design governance.
- Present AI solution roadmaps and recommendations to executive leadership.





MLOps &

- Operational Excellence :
- Establish MLOps best practices for CI/CD, model versioning, monitoring, and retraining.
- Implement model observability, explainability, and performance tracking frameworks.
- Ensure scalability, reliability, security, and compliance of AI systems.
- Define standards for AI deployment, testing, and production support.

Innovation &

- Research :
- Stay current with advancements in Generative AI, LLMs, AI Agents, Deep Learning, and emerging AI frameworks.
- Conduct proof-of-concepts (POCs) and innovation initiatives.
- Drive AI adoption across business units and identify opportunities for automation and optimization.

Required Qualifications :

- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or related field.
- 10 years of IT experience with 5 years in AI/ML architecture or technical leadership roles.
- Robust expertise in Machine Learning, Deep Learning, Statistical Modeling, and Data Science methodologies.

Tech Stack :

- Frameworks: TensorFlow, PyTorch, Scikit-learn, Keras, Hugging Face.
- Languages: Python, SQL, Java, Scala, or C#.
- Cloud: Microsoft Azure AI Services, AWS SageMaker, Google Vertex AI.
- Infrastructure: Docker, Kubernetes, Big Data technologies.

📌 TCS - AI/ML Architect - Generative AI (India)
🏢 TCS
📍 India