Generative AI Engineer

Job Title: GenAI Engineer

Duration: 1-Year Assignment with possibility of extension

Equipment: Candidate must use their own laptop

Key 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

Technical Skills & Platforms

· 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.

Generative AI & Agent Systems

· Hands-on experience with LLM-based system development, agent orchestration, and tool automation using frameworks such as:

o LangChain

o LangGraph

o Semantic Kernel

o 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.

MLOps, LLMOps & DevOps

· 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.

Cloud, Security & Governance

· 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.

Collaboration & Delivery

· 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.

Preferred Certifications & Training

· 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

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