Ai Ml Engineer

Required Qualifications

• 3 years of progressive experience in software engineering, data, or big datarelated roles.

• 2+ years of hands-on experience in AI/ML-focused roles, with deep expertise in applied machine learning.

• Advanced knowledge and practical experience in machine learning and natural language processing (NLP), particularly large language models (LLMs), including their integration, optimization, and evaluation.

• Proven experience building and deploying LLM-based applications, including prompt engineering, context management, embeddings, and retrieval strategies.

• Demonstrated success delivering enterprise-scale chatbots, conversational AI systems, or virtual assistants.

• Strong experience with CI/CD pipelines, containerization technologies (Docker, Kubernetes), and ML lifecycle tooling such as MLflow or equivalent.

• Solid understanding of AI governance, compliance, bias mitigation, explainability, and responsible AI practices.

• Strong knowledge of cloud security, identity management (Azure AD/Entra ID), data governance, and enterprise risk controls.

• Experience leading or significantly contributing to large-scale AI modernization or transformation initiatives.

• Proficiency in Python and modern AI development frameworks for building scalable, production-ready AI systems.

Key Responsibilities

• Architect, design, and deliver scalable GenAI and ML solutions across the full project lifecycle, from concept and experimentation to production deployment and optimization.

• Design and build enterprise-grade conversational AI platforms, including RAG-based systems and agentic workflows, leveraging Azure OpenAI, Azure AI Foundry, and Azure AI services.

• Apply advanced prompt engineering, embeddings, vector search, fine-tuning strategies, and tool/function calling to optimize LLM-driven solutions.

• Design and implement AI/ML models with feedback loops, automated retraining, and fine-tuning pipelines to continuously improve accuracy and relevance.

• Implement retrieval-augmented generation (RAG) techniques to ensure AI outputs are accurate, contextual, and grounded in approved data sources.

• Build and maintain CI/CD pipelines, monitoring, and lifecycle management for AI and LLM workloads in production environments.

• Stay at the forefront of LLM and agentic AI advancements, incorporating modern techniques such as few-shot learning, structured outputs, Model Context Protocol (MCP), and AI orchestration frameworks (e.g., Agent SDKs).