Associate Engineer, AI and Agentic

About the Role

The Associate Engineer - AI and Agentics is responsible for building foundational AI-enabled applications, prototypes, prompts, tests, documentation, and agentic automations while learning SDLC best practices, security expectations, and responsible AI standards.

Key Responsibilities

• Assist in building simple AI-enabled prototypes, internal tools, and workflow automations.
• Translate clearly defined requirements into small software components, scripts, prompts, test cases, and configuration changes.
• Support prompt engineering, basic agent workflows, retrieval-assisted use cases, and API integrations.
• Use approved AI-assisted development tools (e.g., coding copilots or agentic coding tools) to learn, prototype, document, and improve code with appropriate review.
• Write and execute basic unit tests, validation checks, and troubleshooting steps.
• Maintain clear documentation, including setup notes, design summaries, prompt/spec notes, user instructions, and operational runbooks.
• Participate in code reviews, demos, testing sessions, backlog discussions, and team learning activities.
• Follow company policies for data privacy, information security, access control, compliance, and responsible use of AI.

Requirements & Tech Stack

• Foundational programming ability in Python, JavaScript/TypeScript, Java, or a similar language.
• Basic understanding of software development fundamentals, including data structures, APIs, Git, debugging, testing, and documentation.
• Introductory knowledge of AI, machine learning, generative AI, LLMs, prompt engineering, or data science concepts.
• Ability to learn and apply approved AI-assisted development tools responsibly, with review and coaching.
• Ability to quickly acquire new knowledge, apply critical thinking, maintain attention to detail, and ask insightful questions.
• Clear written and verbal communication skills.
• Ability to apply principles of responsible AI, data privacy, information security, and ethical technology practices.

Qualifications

• Candidates with early experience in software engineering, data science, automation, or related technical work gained through internships, academic projects, research assignments, open-source contributions, hackathons, or personal portfolio work.
• A portfolio, GitHub repository, class project, internship deliverable, or capstone project demonstrating coding, automation, AI, data, or problem-solving skills.

Education

• Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related field; or
• Equivalent combination of education and experience.

Preferred

• Basic familiarity with cloud platforms, databases, REST APIs, notebooks, or data tools.