Agentic AI Developer

Key Responsibilities • Develop and prototype agentic AI applications and workflows using large language models and modern AI frameworks. • Build AI agents capable of reasoning, tool use, task orchestration, information retrieval, and interaction with external systems and APIs. • Develop and integrate LLM-powered applications, including retrieval-augmented generation (RAG), multi-agent systems, and AI-assisted automation. • Design and implement backend services and APIs to support AI applications and model integrations. • Integrate LLMs with structured and unstructured data sources, APIs, databases, and software tools. • Develop evaluation and validation workflows to measure AI model and agent performance, accuracy, reliability, and tool-use behavior. • Perform data processing, transformation, and analysis to support AI development and evaluation. • Experiment with prompt engineering, model configurations, retrieval techniques, and agent architectures to improve system performance. • Write clean, maintainable, and well-tested code using modern software engineering practices. • Troubleshoot application, model integration, and data pipeline issues. • Participate in code reviews, testing, documentation, and continuous integration/deployment processes. • Research emerging developments in generative AI, LLMs, agent frameworks, and AI development tools and apply relevant technologies to engineering projects. • Collaborate with engineers and other technical stakeholders to translate requirements into functional AI solutions.Qualifications • Bachelor's degree in Computer Science, Software Engineering, Data Science, Information Systems, or a related technical field, or equivalent practical experience. • Strong programming skills in Python and familiarity with at least one additional programming language such as JavaScript/TypeScript, Java, C/C++, or Rust. • Experience with agentic AI or LLM orchestration frameworks such as LangChain, LangGraph, CrewAI, or similar technologies. • Experience developing multi-agent systems or AI applications involving autonomous task execution and tool use. • Experience with OpenAI APIs, Hugging Face, Ollama, or other commercial or open-source LLM platforms. • Familiarity with RAG pipelines, vector databases, semantic search, or FAISS. • Experience with machine learning frameworks such as PyTorch, TensorFlow, or scikit-learn. • Experience with backend frameworks such as FastAPI, Flask, or Node.js. • Familiarity with SQL and/or NoSQL databases. • Experience with Docker, CI/CD, GitHub Actions, or cloud platforms such as AWS, Google Cloud, or Azure. • Exposure to AI model evaluation, benchmarking, automated validation, or data quality workflows. • Experience using AI-assisted software development tools such as GitHub Copilot, Cursor, Claude Code, or similar tools. • Demonstrated interest in generative AI through academic projects, hackathons, internships, research, or open-source contributions.