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).
  
• Define success metrics aligned with business objectives and continuously evaluate, measure, and improve model quality and system performance.
  
• Establish design standards, reference architectures, and best practices for enterprise AI systems.
  
• Ensure AI solutions meet enterprise requirements for security, reliability, scalability, governance, and compliance.
  
• Evaluate, prototype, and adopt emerging AI patterns, frameworks, and Azure capabilities.
  
• Maintain familiarity with frontend integrations for chat interfaces across web applications, Microsoft Teams, and Copilot.
  
• Serve as a technical mentor for senior and junior engineers, setting a high bar for engineering excellence.
  
• Partner closely with product leaders, architects, and business stakeholders to translate business requirements into scalable AI solutions.
  
• Communicate complex AI concepts and architectural decisions effectively to both technical and non-technical stakeholders.