AI Engineer | Generative AI | LLMs | GitHub Copilot | Claude Code

AI Engineer - Generative AI | LLMs | Enterprise AI Solutions

AI Engineer with experience designing, developing, and implementing enterprise AI solutions using Large Language Models (LLMs) and Generative AI technologies.

Strong background in building intelligent applications, AI assistants, and workflow automation solutions that improve developer productivity and business processes.

Experienced in integrating AI capabilities into existing enterprise platforms using modern software engineering practices, cloud technologies, and scalable microservice architectures.

Comfortable working across the full software development lifecycle, from requirements gathering and solution design through to development, testing, deployment, and production support.

Experienced collaborating with cross-functional teams to deliver secure, scalable, and production-ready AI solutions aligned with business objectives.

Core Skills

• Python, JavaScript/TypeScript
• GitHub Copilot, Claude Code
• OpenAI, Azure OpenAI, Anthropic Claude
• LangChain, LangGraph, Semantic Kernel
• Prompt Engineering, RAG, Semantic Search
• Vector Databases (Pinecone, ChromaDB, Azure AI Search)
• REST APIs, Microservices
• Azure, Docker, Kubernetes
• GitHub Actions, Azure DevOps
• CI/CD, AI Governance

Key Responsibilities

• Design, develop, and deploy AI-powered applications using LLMs and Generative AI.
• Build AI assistants, copilots, and intelligent workflow automation solutions.
• Develop Retrieval-Augmented Generation (RAG) solutions using enterprise knowledge sources and vector databases.
• Integrate AI models and services into enterprise applications through APIs and cloud-native architectures.
• Implement prompt engineering strategies to improve model accuracy and response quality.
• Build scalable, secure, and maintainable AI solutions following software engineering best practices.
• Collaborate with business stakeholders, architects, and engineering teams to deliver AI-driven solutions.
• Support AI governance, model evaluation, monitoring, and responsible AI practices.
• Contribute to CI/CD pipelines and cloud deployments for AI-enabled applications.
• Promote the effective adoption of AI-assisted development tools and engineering best practices.