AI/ ML Engineering

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.