4-6 years of experience in enterprise-scale AI platform design and operationalization
Hands-on expertise in agentic AI, LLMs, and Azure solutions
Proficient in Python for AI/ML applications
Experience with ML frameworks like TensorFlow and PyTorch
Familiarity with AI workflow orchestration tools such as KubeFlow and BentoML
Bachelor's degree in Computer Science, Data Science, or related field
Relevant certifications in Machine Learning or Data Science
Responsibilities
Design, build, and deploy AI agents and multi-agent systems using Azure AI services
Develop and operationalize agentic workflows integrating AI with Microsoft 365 and enterprise APIs
Implement enterprise RAG solutions to enhance knowledge retrieval
Build production-ready AI applications leveraging LLMs and RAG pipelines
Establish observability and security for AI applications in production
Optimize AI applications for performance and cost efficiency
Collaborate with teams to identify AI opportunities and prototype solutions
Benefits
Opportunity to work with cutting-edge AI technologies
Collaborative environment with cross-functional teams
Focus on responsible AI practices and governance
Access to professional development and certification opportunities
Engagement in innovative projects that impact business functions
Full Job Description
Lead AI/ML Engineer Job requirements
Experience Range: With at least 4 to 6 years of experience designing, building, and operationalizing enterprise-scale AI platforms, including hands-on expertise in agentic AI, LLMs, and Azure-based solutions. Key Responsibilities:
Design, build, and deploy AI agents and multi-agent systems using Azure AI services, Azure OpenAI, and modern agentic frameworks such as Semantic Kernel, LangChain, LangGraph, and AutoGen.
Develop and operationalize agentic workflows for business functions, integrating AI solutions with Microsoft 365, Teams, SharePoint, Dynamics 365, Power Platform, and enterprise APIs.
Implement enterprise RAG solutions utilizing Azure AI Search, vector search, hybrid search, and semantic ranking to enhance knowledge retrieval and business decision-making.
Build production-ready AI applications leveraging LLMs, RAG pipelines, tool/function calling, memory systems, and workflow orchestration for scalable, secure, and reliable enterprise deployment.
Establish observability, guardrails, responsible AI controls, evaluation, security, and monitoring for AI applications in production environments.
Optimize agent and LLM applications for latency, accuracy, reliability, scalability, security, and cost, applying advanced techniques and metrics.
Collaborate with cross-functional teams to identify AI opportunities, rapidly prototype solutions, and iterate based on user feedback.
Contribute to internal best practices around agent architecture, prompting, RAG, model selection, evaluation, and AI engineering standards.
Required Skills:
Hands-on experience with Azure AI services, Azure OpenAI, and Azure AI Foundry
Expertise in agentic frameworks (Semantic Kernel, LangChain, LangGraph, AutoGen)
LLM application development and prompt engineering
Enterprise RAG architectures and vector search technologies
Integration with Microsoft 365, Teams, SharePoint, Dynamics 365, Power Platform, and APIs
Python programming for AI/ML solutions
ML frameworks (TensorFlow, PyTorch, Sci-Kit Learn, Keras)
AI workflow orchestration tools (KubeFlow, BentoML)
Model evaluation and observability tools (Evidently AI, Great Expectations)
AI security and governance on Azure
Preferred Skills:
Experience with Azure Functions, Azure Container Apps, Azure Kubernetes Service (AKS), and Azure Storage
Proficiency in R and R Studio for statistical computing
Knowledge of SAS or SPSS for advanced statistical modeling
Familiarity with microservices, event-driven architectures, and enterprise integrations
Expertise in developing reusable agent frameworks, prompt libraries, and deployment patterns
Desired Qualifications:
Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or a closely related discipline
Certification in Machine Learning or Data Science from a recognized institution (e.g., TensorFlow Developer Certificate, AWS Certified Machine Learning Specialty, Microsoft Certified: Azure AI Engineer Associate)
Certification in statistical analysis tools or platforms (e.g., SAS Certified Statistical Business Analyst)
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