Job Description :

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), and AI Agents.

- 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, and Cloud Monitoring.

Programming & Development :

- Strong proficiency in Python, SQL, and REST APIs.

- Experience with TensorFlow, PyTorch, Scikit-learn, Hugging Face Transformers, LangChain, LlamaIndex, and Semantic Kernel.

Data & Analytics :

- Experience designing modern data platforms on GCP including BigQuery, Data Lakes, Data Warehousing, Streaming Analytics, Feature Stores, and Vector Databases.

MLOps & DevOps :

- Experience implementing CI/CD Pipelines, Model Monitoring, Feature Engineering Pipelines, Automated Retraining Frameworks, and Infrastructure as Code (Terraform).

- Familiarity with Docker, Kubernetes, GitHub Actions, and Jenkins.