AI Engineer (Jr/Sr)

Role: AI Project Engineer

Job Responsibilities

• Develop and enhance AI/Generative AI applications using Large Language Models (LLMs).
• Design and implement RAG pipelines for intelligent knowledge retrieval and question-answering solutions.
• Build AI agents and agentic workflows for reasoning, planning, tool usage, and task automation.
• Develop and integrate MCP tools to connect AI applications with enterprise systems, databases, APIs, and engineering services.
• Develop end-to-end AI solutions from PoC and prototyping through implementation, testing, deployment, and support .
• Integrate AI capabilities with APIs, databases, backend services, and engineering applications .
• Work with prompt engineering, embeddings, vector databases, and semantic search to improve AI solution quality.
• Evaluate, test, debug, and optimize AI solutions for accuracy, reliability, performance, and scalability .
• Apply good software engineering practices including version control, testing, documentation, and CI/CD .
• Collaborate with cross-functional teams to translate business and engineering requirements into practical AI solutions.

Required Skills & Qualifications

• Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, Computer Engineering , or a related field.
• At least 2-6 years of related working experiences.
• Hands-on experience developing AI/Generative AI solutions , such as LLM applications, RAG systems, AI agents, or AI-driven automation.
• Strong programming skills in Python .
• Good understanding of LLMs, RAG, Agentic AI, prompt engineering, embeddings, and vector databases .
• Experience with REST APIs, backend development, databases, and system integration .
• Familiarity with SQL, Git/GitHub, testing, debugging, and software development lifecycle practices .
• Strong analytical, problem-solving, and troubleshooting skills.

Preferred Skills

• Experience with Azure OpenAI, OpenAI, Claude, Gemini , or open-source LLMs.
• Experience with LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel , or similar Agentic AI frameworks.
• Understanding of MCP (Model Context Protocol) and tool integrations.
• Experience with FastAPI, Docker/containers, cloud platforms, DevOps, or CI/CD .
• Exposure to PyTorch, TensorFlow, LLMOps, AI evaluation, model benchmarking, or AI application monitoring .
• Experience taking AI solutions from PoC/prototype to deployment .