Location: India
Job Type: Full Time/Lateral
Status: Active
Posted: 2026-04-13 06:02:52
Exp Range - 5 to 9
Location - Pan India
Required Skills & Qualifications
• Proficiency in Python, TensorFlow or PyTorch, and SQL for ML development.
• Hands-on experience building and deploying ML solutions on one or both of:
◦ Azure: Azure Machine Learning, Azure OpenAI, Azure AI Search, Azure Data Lake, Copilot Studio and Azure Functions.
◦ GCP: Vertex AI, BigQuery, Cloud Storage, and Cloud Run or GKE.
• Strong background in machine learning, including supervised/unsupervised learning and model evaluation techniques.
• Familiarity with embedding models, vector search, and prompt-based LLM interactions, agents, and agentic AI workflows.
• Knowledge of data preprocessing, model versioning, CI/CD pipelines, and deployment workflows in cloud environments.
• Proficiency in Python-based backend frameworks such as FastAPI or Flask, or Node.js for building APIs and services.
• Working knowledge of relational databases (PostgreSQL, MySQL) and NoSQL databases (MongoDB, Firestore, Cosmos DB).
• Understanding of authentication and authorization mechanisms (OAuth2, JWT, API keys).
• Hands-on experience with React.js or Angular for building interactive web applications.
• Proficiency in HTML5, CSS3, and JavaScript/TypeScript.
• Experience consuming REST APIs and managing application state in frontend frameworks.
• Bachelor's or Master's degree in Machine Learning, Data Science, Computer Science, Software Engineering, or a related field.
Preferred Qualifications
• Experience building full-stack AI applications that integrate ML model outputs into user-facing interfaces.
• Experience working with RAG systems, conversational AI, or tool-calling agents using Azure OpenAI, Vertex AI, or external LLM integrations.
• Exposure to model monitoring, automated retraining workflows, and scalable serving architectures.
• Familiarity with containerization (Docker) and orchestration tools such as AKS (Azure) or GKE (GCP).
• Experience with real-time inference systems and multi-cloud deployment strategies.
AI/ML Engineering