Sr. Data Scientist

• Define reference architecture for agentic AI solutions across Copilot Studio and Azure AI Foundry 

• Establish design patterns for:  

• Retrieval-Augmented Generation (RAG) 

• Multi-agent orchestration 

• Enterprise integrations (SAP, Salesforce, Databricks, SharePoint, Azure Ecosystem) 

• Guide use-case prioritization and platform selection (Studio vs Foundry vs hybrid)  

2. AI Agent Development & Delivery 

• Build and deploy production-grade AI agents, including:  

• Knowledge & troubleshooting agents 

• Operational / workflow automation agents 

• Data and analytics-driven agents 

• Implement:  

• Prompt engineering and evaluation strategies 

• Agent workflows and orchestration logic 

• API, connector, and system integrations 

3. Platform Foundation & Governance 

• Establish enterprise AI guardrails, including:  

• Security, RBAC, and identity integration (Entra ID) 

• Data access boundaries and governance 

• Audit logging and monitoring frameworks 

• Define and implement:  

• Agent lifecycle (draft → pilot → production → retirement) 

• CI/CD pipelines and deployment standards 

4. Azure AI & Microsoft Ecosystem Implementation 

• Configure and deploy:  

• Azure OpenAI / model endpoints 

• Azure AI Search (vector + semantic retrieval) 

• Application Insights / Log Analytics monitoring 

• Build and support:  

• Copilot Studio environments and orchestration layers 

• Azure AI Foundry-based custom agent services 

5. Co-Development & Enablement 

• Work directly with consultants and internal teams to:  

• Co-build pilot agents 

• Facilitate adoption and value-based outcomes 

• Partner with data engineering team on: 

• Data product and semantic layer alignment 

• AI orchestration 

• Observability and feedback loops 

• Enable internal teams on:  

• Agent design patterns 

• Responsible AI practices 

• Ongoing support and scaling 

-Technical Expertise 

• Strong experience with:  

• Azure AI services (Foundry, Cognitive Services, AI Search) 

• Microsoft Copilot Studio / Power Platform 

• Cloud-native architecture (Azure) 

• Experience building:  

• Conversational AI / chatbot / agent solutions 

• RAG pipelines and LLM-based applications 

• API integrations, MCP frameworks, and enterprise workflows 

-Architecture & Engineering 

• Proven ability to design:  

• Scalable, secure AI platforms 

• Hybrid architectures (low-code + pro-code) 

• Experience with:  

• MLOps and CI/CD pipelines 

• Monitoring and observability (App Insights, logging, tracing) 

• Secure cloud networking and identity 

• Validating and optimizing AI systems 

• Evaluating and selecting AI platforms and tools (cloud-native and third-party) 

• Defining design patterns, standards, and guardrails for AI solutions 

• Balancing rapid experimentation with maintaining platform consistency and avoiding fragmentation 

-Governance & Responsible AI 

• Experience implementing:  

• AI governance frameworks 

• Data security, privacy and compliance controls 

• Lifecycle management and deployment gating 

• Understanding of regulatory considerations (e.g., data privacy, AI compliance risks) 

• Experience with auditing, monitoring, and incident response for AI systems in production 

-Experience 

• 5+ years in cloud / data / AI engineering or architecture roles 

• 5+ years in software development or data engineering 

• Hands-on experience building LLM-based applications or agentic AI solutions 

-Preferred Qualifications 

• Microsoft certifications (Azure AI Engineer, Azure Architect, etc.) 

• Experience integrating AI agents with:  

• Microsoft Teams / M365 

• ERP / CRM systems (SAP, Salesforce) 

• Familiarity with Databricks or lakehouse architectures 

• Experience delivering multi-phase AI programs (pilot → scale) 

-Engagement Model 

• First 90 days: co-build solutions with Microsoft Partner 

• Establish AI foundation and standards 

• Drive adoption and value realization 

• Create support model and roadmap enterprise AI solutions 

-What Success Looks Like 

• Production-ready AI agents deployed within Microsoft 365 

• Scalable AI agent platform established (governed, monitored, secure) 

• Internal teams enabled to independently build and operate agents 

• Clear pipeline of business-driven AI use cases 

• Consistent use of defined AI platforms and patterns across teams  

• Reduced duplication and fragmentation across AI solutions 

• Clear intake, prioritization, and decision-making for AI use cases 

• Align with vendor best practice to build, take ownership, and continue building value for the organization!