AI/ML Engineer (freelancer / consultant)

Lead end-to-end technical implementation of AI-driven projects using Microsoft AI tools: Azure

OpenAI, Copilot Studio, and Bot Framework.

Design and develop intelligent copilots, multi-turn chatbots, and custom GPT solutions integrated

within enterprise tools such as Microsoft Teams, SharePoint, and Dynamics 365.

Translate business requirements into technical architecture and AI flows using OpenAI APIs, prompt

engineering, and integration with enterprise systems.

Leverage AI Foundry to manage the AI lifecycle including model selection, deployment, monitoring,

and optimization.

Architect AI/ML solutions that use Retrieval-Augmented Generation (RAG), semantic search, and

contextual memory frameworks (LangChain, Semantic Kernel, etc.).

Collaborate with product owners and business analysts to identify high-value use cases and define

solution roadmaps.

Develop and execute POCs and MVPs with hands-on coding, configuration, and orchestration of LLMs

and chatbot pipelines.

Integrate with enterprise data sources via APIs, GraphQL, and Microsoft Graph to create holistic user

experiences.

Mentor junior developers and work with DevOps teams to ensure stable deployment, CI/CD, and

performance monitoring.

Create documentation and reusable components/templates for repeated use across the organization.

Stay current on Microsoft’s AI advancements and recommend tools, features, or practices that

improve time-to-value and performance.

Must-Have Skills:

6–8 years of overall experience, including 5+ years in AI/ML or Conversational AI

Deep hands-on knowledge of:

Azure OpenAI services and APIs

Copilot Studio for building Microsoft 365-integrated assistants

Microsoft Bot Framework SDK/Composer for chatbot development

Prompt engineering for LLM optimization

Strong Python or Node.js development skills (for AI orchestration and integration)

Experience with enterprise system integration using APIs (Microsoft Graph, REST, JSON, OAuth)

Familiarity with Azure ML, Azure Cognitive Services, and Azure DevOps

Ability to design RAG-based architectures, manage embeddings, and leverage vector databases (e.g.,

Azure AI Search)

Strong understanding of natural language processing (NLP) and foundational models (GPT, BERT)

Excellent communication, leadership, and stakeholder engagement capabilities.

Good-to-Have Skills:

Experience with Semantic Kernel or LangChain

Working knowledge of AI Foundry for orchestrating AI pipelines

Familiarity with Copilot extensibility and Teams App Studio

Exposure to M365 Copilot APIs and custom plugin creation

Knowledge of Responsible AI, data security, and compliance principles

Familiarity with containerized deployment (Docker, Kubernetes)

Experience in building dashboards and analytics (Kibana, Grafana) to visualize bot usage and

performance

Basic understanding of Power Platform (Power Automate, Power Apps) and its integration with AI