Agentic AI Developer

Key ResponsibilitiesDevelop 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.QualificationsBachelor'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.