Key Responsibilities

- Design and architect enterprise-grade AI/ML and Generative AI solutions on Google Cloud Platform.
- Develop AI strategies, reference architectures, and implementation roadmaps aligned with business objectives.
- Lead the deployment of machine learning models using Google Vertex AI and related GCP services.
- Design and implement Generative AI solutions utilizing foundation models, LLMs, and Retrieval-Augmented Generation (RAG) architectures.
- Define AI governance, security, compliance, and responsible AI frameworks.
- Architect scalable data pipelines and AI platforms for model development, training, and deployment.
- Guide engineering teams on AI best practices, model lifecycle management, and MLOps methodologies.
- Evaluate and select AI technologies, frameworks, and cloud services to meet business requirements.
- Optimize AI workloads for performance, scalability, reliability, and cost efficiency.
- Collaborate with cross-functional teams to integrate AI capabilities into enterprise applications.
- Establish monitoring, observability, and model performance frameworks.
- Drive innovation initiatives leveraging emerging AI technologies and Google Cloud services.

Required Skills & Qualifications AI & Machine Learning

- Strong expertise in Machine Learning, Deep Learning, and Generative AI concepts.
- Experience designing AI solutions using:





- Large Language Models (LLMs)

- Foundation Models

- Prompt Engineering
- Fine-Tuning Techniques
- Retrieval-Augmented Generation (RAG)
- AI Agents and Agentic AI systems
- Experience with NLP, Computer Vision, Recommendation Systems, and Predictive Analytics.

Google Cloud Expertise
- Hands-on experience with:

- Vertex AI

- Vertex AI Search

- Vertex AI Agent Builder

- Gemini Models

- BigQuery ML

- Cloud Storage

- Dataflow

- Dataproc

- Pub/Sub

- Cloud Functions

- Cloud Run

- Kubernetes Engine (GKE)

- IAM

- Cloud Monitoring

Programming & Development
- Robust proficiency in:

- Python

- SQL

- REST APIs

- Experience with

- TensorFlow

- PyTorch
- Scikit-learn

- Hugging Face Transformers

- LangChain

- LlamaIndex

- Semantic Kernel

Data & Analytics
- Experience designing modern data platforms on GCP.

- Knowledge of

- BigQuery

- Data Lakes

- Data Warehousing

- Streaming Analytics

- Feature Stores

- Vector Databases

MLOps & DevOps
- Experience implementing:

- CI/CD Pipelines

- Model Monitoring

- Feature Engineering Pipelines

- Automated Retraining Frameworks

- Infrastructure as Code (Terraform)

- Familiarity with

- Docker

- Kubernetes

- GitHub Actions

- Jenkins

📌 GCP AI Architect - S (Bengaluru)
🏢 Tata Consultancy Services
📍 Bengaluru