Job Summary
| Experienced AIML Architect to design and implement cutting-edge AI/ML solutions leveraging Google Cloud Platform (GCP) and other modern technologies. The ideal candidate will have strong expertise in building scalable models, deploying them in production, and driving innovation across multiple industry verticals. |
| • Design and implement AI/ML solutions for complex business problems across domains like Banking, Finance, Telecom, Retail, and Technology. Develop and optimize Machine Learning and Deep Learning models using frameworks such as TensorFlow, Keras, PyTorch. Build and maintain scalable data pipelines and ML workflows using Python, Spark, PySpark, and GCP services. Deploy and monitor models on public cloud platforms (AWS/Azure/GCP), with hands-on experience in Vertex AI, BigQuery, Cloud Composer. Transform prototypes into robust, production-ready solutions. Implement CI/CD principles for ML Ops, ensuring seamless integration and deployment. Work with containerization and orchestration tools like Docker and Kubernetes. Apply advanced statistical analysis, text mining, and machine log processing techniques. Collaborate with cross-functional teams to ensure alignment with business goals. |
| Technical / Functional Skills:- AI/ML Development: TensorFlow, Keras, PyTorch, Scikit-learn, Spark ML. Programming: Python (expert), R (working knowledge), Spark. Data Handling: Pandas, NumPy, PySpark. Cloud Platforms: Google Cloud Platform (Vertex AI, BigQuery, AI/ML services), AWS/Azure experience is a plus. ML Ops: CI/CD pipelines, model deployment, monitoring. Statistical Analysis: Strong foundation in statistics and data-driven decision-making. Domain Knowledge: Exposure to supervised, unsupervised, and reinforcement learning. |
Key Responsibilities
2. Creation of solution and architectural views (logical| conceptual| development| execution| infrastructure & operations architecture)
3. To collaborate with business and technical stakeholders, including leaders, project managers, and development teams, to understand and prioritize requirements while defining the architecture.
4. To ensure knowledge up-gradation and work with new and emerging products/technologies
5. To drive innovation by exploring and recommending new solutions within the organization.
6. To contribute towards white/technical papers and knowledge base