AI Engineer (FDE – Full Development Engineer) - Part-Time

Job Description & Requirements

About the Role

We are looking for an experienced AI Engineer (FDE) who can design, architect, and develop AI-powered solutions for diverse business use cases. The ideal candidate should possess strong GenAI and AI/ML expertise, be capable of solutioning across multiple AI technology stacks, and have excellent business communication skills to engage with stakeholders and translate business requirements into scalable AI solutions.

Key Responsibilities

• Design, architect, and implement AI/GenAI solutions for enterprise business use cases.
• Engage with business stakeholders to understand challenges and recommend AI-driven solutions.
• Build end-to-end AI applications using modern AI frameworks and cloud platforms.
• Evaluate and adapt to different AI technologies, models, and platforms based on business needs.
• Develop scalable RAG (Retrieval-Augmented Generation), AI Agents, and LLM-based applications.
• Integrate AI solutions with enterprise systems and APIs.
• Optimize AI model performance, cost, scalability, and security.
• Collaborate with cross-functional teams including Product, Engineering, Data Science, and Business teams.
• Stay updated with the latest advancements in AI, GenAI, LLMs, AI Agents, and cloud AI services.
• Prepare solution architecture documents, technical proposals, and client presentations.

Required Skills

• 4+ years of experience in AI/ML or Generative AI solution development.
• Strong experience with LLMs (OpenAI, Claude, Gemini, Llama, Mistral, etc.).
• Hands-on experience building RAG pipelines, AI Agents, Agentic AI, and prompt engineering.
• Experience with AI frameworks such as:
• LangChain
• LangGraph
• LlamaIndex
• Semantic Kernel
• CrewAI / AutoGen (preferred)
• Strong programming skills in Python.
• Experience with vector databases such as Pinecone, ChromaDB, Milvus, Weaviate, or FAISS.
• Experience with cloud AI platforms (Azure AI Foundry, Azure OpenAI, AWS Bedrock, Google Vertex AI, etc.).
• Good understanding of MLOps, model deployment, monitoring, and AI governance.
• Experience integrating AI solutions using REST APIs and microservices.
• Familiarity with containerization (Docker, Kubernetes) is an advantage.