Job Description :

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.

- Strong 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.