Artificial Intelligence Developer
Summary
Leads architecture for an AI-driven SDLC platform: designing multi-agent orchestration (Azure AI/OpenAI, Semantic Kernel, Copilot Studio, AutoGen, CrewAI, LangChain), RAG/vector-search knowledge systems, HITL governance, and enterprise integrations, while mentoring AI engineers. Requires 5+ years in GenAI/agentic AI with Python and/or C#.
Title: AI developer
Key Responsibilities
- Define the end-to-end architecture for an AI-driven SDLC platform that supports software delivery from requirements through deployment.
- Design and implement multi-agent orchestration frameworks using Azure AI, Semantic Kernel, Copilot Studio, AutoGen, CrewAI, LangChain, or similar agent technologies.
- Architect AI capabilities for automated requirements analysis, user story generation, solution design creation, fit-gap assessment, technical documentation generation, and test automation.
- Design and implement Retrieval-Augmented Generation (RAG), vector search, knowledge graph, and enterprise knowledge management architectures.
- Establish Human-in-the-Loop (HITL) controls and governance mechanisms to ensure quality, compliance, traceability, and auditability of AI-generated outputs.
- Define integration patterns between AI agents and enterprise platforms such as Azure, GitHub, Azure DevOps, Jira, CI/CD pipelines, enterprise repositories, and business applications.
- Lead architecture decisions related to Responsible AI, security, model governance, prompt engineering standards, and regulatory compliance.
- Provide technical leadership, architecture governance, and mentorship to AI engineers, software architects, and development teams.
- Develop reusable AI accelerators, frameworks, reference architectures, and implementation patterns to drive adoption and scalability.
- Continuously evaluate emerging AI technologies and identify opportunities to enhance platform capabilities and SDLC automation.
Required Skills & Experience
- 5+ years of experience designing and implementing AI, GenAI, Machine Learning, or Agentic AI solutions.
- Strong expertise in enterprise AI architecture, large language models (LLMs), autonomous agents, and AI orchestration frameworks.
- Hands-on experience with Azure AI Foundry, Azure OpenAI, Semantic Kernel, Copilot Studio, LangChain, AutoGen, CrewAI, or equivalent technologies.
- Deep understanding of RAG architectures, vector databases, embeddings, knowledge graphs, semantic search, and enterprise knowledge systems.
- Proficiency in Python and/or C# with experience building AI-enabled applications and services.
- Experience designing API-driven, event-based, cloud-native, and microservices-based architectures.
- Strong knowledge of GitHub Copilot, Azure DevOps, CI/CD pipelines, DevSecOps, and SDLC best practices.
- Experience implementing AI governance, security, compliance, risk management, and Responsible AI principles.
- Excellent stakeholder management, leadership, and executive communication skills.
Preferred Qualifications
- Experience building AI-powered SDLC accelerators, engineering copilots, or software development automation platforms.
- Azure AI Engineer Associate or equivalent AI/ML certifications.
- Experience implementing Copilot, Agentic AI, or intelligent automation solutions in enterprise environments.
- Familiarity with Dynamics 365 CRM, Power Platform, Dataverse, or enterprise SaaS platforms.
Skills
- Agentic AI
- AI
- API
- AutoGen
- Automation
- Azure
- Azure DevOps
- CI/CD
- Cloud
- Cloud Native
- CrewAI
- CRM
- C#
- Dataverse
- DevOps
- DevSecOps
- Embeddings
- Generative AI
- GitHub
- Github Copilot
- Jira
- LangChain
- LLM
- Machine Learning
- Microservices
- Microsoft Copilot
- Microsoft Dynamics
- OpenAI
- Power Platform
- Prompt Engineering
- Python
- RAG
- Regulatory Compliance
- SaaS
- SDLC
- Semantic Kernel
- Semantic Search
- Solution Design
- Stakeholder Management
- Test Automation
- Vector Databases
- Vector Search