AI/ ML Engineering

Role: AI/ ML Engineering Experience: 3 to 5 yrs Work location: Hyderabad Interview mode: Virtual Notice period: Immediate Joiner Key Responsibilities: Design and implement RAG pipelines and AI agentic systems using cutting-edge LLM frameworks. Fine-tune open-source LLMs and develop narrow, domain-specific models. Build and maintain ML pipelines using MLFlow and ensure reproducibility, auditability, and version control. Collaborate with cross-functional teams to deploy ML systems into scalable, secure, and production-ready environments. Containerize and serve models using Docker, Kubernetes, and FastAPI. Automate CI/CD workflows using Azure DevOps, with integrated monitoring and alerts. Integrate authentication and authorization flows using Azure AD and Microsoft Graph API. Optimize deployed models for latency, cost-efficiency, and operational maintainability. Required Skills & Experience: Strong foundation in Computer Science, software architecture, and distributed systems. Proficiency in Python, including both object-oriented and functional programming paradigms. Hands-on experience with open-source LLMs, embedding models, and vector databases. Practical implementation of RAG pipelines and LLM agentic systems. Strong working knowledge of MLOps tooling (e.g., MLFlow), model versioning, and reproducible experiments. Experience deploying ML systems using Docker, Kubernetes, and FastAPI or equivalent frameworks. Proven experience working in Azure cloud ecosystem: Azure DevOps for build/release automation. Azure GraphAPI for accessing organizational data. Secure identity flows using Azure AD. Role: AI/ ML Engineering Experience: 3 to 5 yrs Work location: Hyderabad Interview mode: Virtual Notice period: Immediate Joiner Key Responsibilities: Design and implement RAG pipelines and AI agentic systems using cutting-edge LLM frameworks. Fine-tune open-source LLMs and develop narrow, domain-specific models. Build and maintain ML pipelines using MLFlow and ensure reproducibility, auditability, and version control. Collaborate with cross-functional teams to deploy ML systems into scalable, secure, and production-ready environments. Containerize and serve models using Docker, Kubernetes, and FastAPI. Automate CI/CD workflows using Azure DevOps, with integrated monitoring and alerts. Integrate authentication and authorization flows using Azure AD and Microsoft Graph API. Optimize deployed models for latency, cost-efficiency, and operational maintainability. Required Skills & Experience: Strong foundation in Computer Science, software architecture, and distributed systems. Proficiency in Python, including both object-oriented and functional programming paradigms. Hands-on experience with open-source LLMs, embedding models, and vector databases. Practical implementation of RAG pipelines and LLM agentic systems. Strong working knowledge of MLOps tooling (e.g., MLFlow), model versioning, and reproducible experiments. Experience deploying ML systems using Docker, Kubernetes, and FastAPI or equivalent frameworks. Proven experience working in Azure cloud ecosystem: Azure DevOps for build/release automation. Azure GraphAPI for accessing organizational data. Secure identity flows using Azure AD.