This role is explicitly focused on building agentic systems from scratch, including the design and development of autonomous AI agents and enterprise copilots, with hands-on experience in Microsoft 365 Copilot and Copilot Studio ecosystems as a core requirement.
Agentic AI Architecture & Engineering: Build AI agents and multi-agent systems from scratch, including agent orchestration, autonomy design, tool-use, memory management, and decision frameworks.
Enterprise Copilot Development: Design, build, and scale enterprise copilots using Microsoft Copilot Studio and Microsoft 365 Copilot, enabling intelligent task automation and knowledge workflows.
Build AI agents & copilots on Microsoft Copilot Studio
Develop automated workflows using Power Automate / Logic Apps
Integrate AI agents with enterprise systems via APIs, connectors, and Azure services
Implement RAG, grounding, semantic search using Azure OpenAI & Azure Cognitive Search
Ensure security, governance, and responsible AI practices
Collaborate with architects, analysts, and business teams
Problem Formulation: Translate business objectives into well-defined data science, ML, and Agentic AI problems; validate OKRs using robust statistical and experimental measures.
Agentic AI & LLM Solutions: Design, build, deploy, and optimize Agentic AI systems (multi-agent workflows, task orchestration, autonomous decision-making) using LLMs for real-world enterprise use cases.
LLM Development & Deployment: Fine-tune, prompt-engineer, evaluate, and productionize LLMs (open-source or proprietary) for use cases such as copilots, RAG pipelines, conversational AI, and intelligent automation.
Data Wrangling & Feature Engineering: Handle structured and unstructured data at scale, including text, documents, and conversational data for LLM-powered solutions.
Insight Generation & Data Storytelling: Convert complex analytical outputs and AI model results into clear, compelling narratives for business and executive audiences.
Technical Decision-Making: Make informed trade-offs on model complexity, iteration depth, experimentation cycles, and time-to-value.
Design Thinking & Innovation: Apply design thinking principles to build user-centric AI products and data solutions.
Mentorship & Leadership: Coach senior data scientists, review architectures, and establish best practices across data science, ML, and GenAI initiatives.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

agentic systemsautonomous AI agentsenterprise copilotsAI architecturemulti-agent systemsPower AutomateLogic AppsAzure OpenAIAzure Cognitive Searchdata wrangling

Soft Skills

problem formulationdata storytellingtechnical decision-makingdesign thinkingmentorshipleadershipcollaboration