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.

Minimum 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.
• Understanding of core computer science concepts, including data structures, algorithms, databases, APIs, and software development principles.
• Hands-on experience with generative AI, large language models, or machine learning through professional experience, internships, research, coursework, or personal projects.
• Familiarity with LLM APIs and/or open-source language models.
• Understanding of concepts such as prompt engineering, RAG, embeddings, vector search, AI agents, and tool/function calling.
• Experience developing or consuming REST APIs and working with structured data.
• Familiarity with Git and collaborative software development workflows.
• Strong analytical, problem-solving, and debugging skills.
• Ability to learn new AI technologies and frameworks quickly and work effectively in a fast-paced engineering environment.