Responsibilities
- Design, build, and deliver end-to-end AI/ML solutions—from experimentation and prototyping to production deployment.
- Develop AI solutions using Azure AI Foundry, Azure OpenAI, Azure Machine Learning, and related Azure AI services.
- Build agent-based architectures using frameworks such as LangChain, LangGraph, Semantic Kernel, and MCP-style orchestration patterns.
- Design and optimize prompt engineering strategies, RAG pipelines, embeddings, vector search, and knowledge-grounding workflows.
- Build, train, evaluate, and deploy classical ML and GenAI models using Azure Machine Learning, including pipelines, feature engineering, model registry, and experiment tracking.
- Implement MLOps and LLMOps practices including CI/CD, automated testing, responsible deployment, model monitoring, drift detection, and performance optimization.
- Integrate AI solutions securely with enterprise systems, APIs, and event-driven architectures.
- Embed Responsible AI principles—fairness, explainability, transparency, and human-in-the-loop controls—into solution design and development.
- Collaborate closely with Data Engineers, AI Architects, Security teams, and business stakeholders to deliver scalable, compliant AI solutions.
- Provide engineering guidance, mentor junior team members, and contribute to reusable components, shared libraries, and engineering best practices.
Requirements
- Strong hands-on experience building and deploying AI solutions on Azure, including Azure AI Foundry, Azure OpenAI, Azure Machine Learning, Azure AI Search, and Cognitive Services.
- Solid understanding of machine learning concepts including feature engineering, model training, evaluation, hyperparameter tuning, and operational deployment.
- Experience deploying both predictive ML and GenAI solutions in enterprise settings.
- Hands-on experience with LLM-based system development, agent orchestration, and tool automation using frameworks such as: LangChain, LangGraph, Semantic Kernel, MCP-style agent communication patterns.
- Experience implementing RAG pipelines, embeddings, vector databases, and document ingestion architectures.
- Strong understanding of LLM constraints, prompt optimization, hallucination mitigation, and output-validation strategies.
- Experience implementing CI/CD for ML and LLM workloads, including testing, monitoring, versioning, and automated deployment.
- Familiarity with Azure DevOps pipelines, Git-based workflows, and cloud-native deployment automation.
- Ability to balance rapid prototyping with strong engineering rigor, reliability practices, and production-readiness.
- Understanding of cloud-native patterns, containerization, and scalable AI infrastructure.
- Knowledge of identity, access management, secrets management, and secure deployment practices for AI systems.
- Familiarity with Responsible AI frameworks and enterprise governance models.
- Ability to translate business problems into practical, scalable AI solutions.
- Strong communication and cross-functional collaboration skills.
- Experience working within Agile environments (Scrum, Kanban) delivering iteratively and incrementally.
Nice to Have
- Databricks Certified Generative AI Engineer Associate, Microsoft Azure AI Engineer Associate, Azure Machine Learning Certification, Azure Data Scientist Associate (optional), MLOps or LLMOps training, LangChain/GenAI specialization coursework.
Additional Information
- Work with cutting-edge AI technologies and modern GenAI frameworks.
- Lead hands-on development of AI systems deployed at enterprise scale.
- Collaborate with cross-functional experts across architecture, engineering, and security.
