Associate AI Solutions Engineer

Primary Responsibilities and Duties

• This role will help design, build, and document AI solutions and collaborate amongst Technology and business groups to support internal teams and advance Achieve's AI strategy.
• AI Solution Development: Build AI-powered applications, agents, and workflows under the guidance of senior engineers to automate manual work and create new capabilities. Contribute to integrating AI responsibly into production systems and existing platforms.
• LLM and Agent Implementation: Develop solutions using large language models, retrieval-augmented generation (RAG), and agentic frameworks. Contribute to shared infrastructure including prompt libraries, evaluation harnesses, and Model Context Protocol (MCP) integrations.
• Enterprise Integration: Help implement integration patterns that connect AI solutions to enterprise systems (CRMs, data warehouses, ticketing platforms, financial systems). Evaluate AI services, models, and technologies under the direction of senior team members.
• Applied Engineering and Delivery: Partner with senior engineers and business stakeholders across Customer Service, Lending, Finance, Legal, HR, and Operations to translate business problems into working solutions. Ship clean, maintainable code. Support evaluations, monitoring, and issue remediation to ensure reliability and quality.
• Education: Bachelor's degree in Computer Science, Engineering, or related field. Coursework or demonstrated interest in AI/ML is a plus.
• Experience: 0-2 years of software engineering experience, with exposure to building AI-powered applications, internships, personal projects, or relevant academic work.
• Technical Skills: Working knowledge of AI-powered application development, including LLM-based systems or traditional ML models. Familiarity with LLM techniques such as prompt engineering, retrieval-augmented generation (RAG), and basic evaluation methods. Exposure to modern AI tooling and frameworks; interest in Model Context Protocol (MCP) and agentic frameworks. Familiarity with APIs, integration patterns, and at least one major cloud platform (AWS, Azure, or GCP). Solid software engineering fundamentals: clean code, testing, version control, and a growing understanding of production reliability.
• Working knowledge of AI-powered application development, including LLM-based systems or traditional ML models.