The Associate AI Engineer supports the design, development, and deployment of AI‑powered solutions that improve business processes, teammate productivity, and customer experiences. This role is ideal for early‑career engineers who are eager to build hands‑on experience with machine learning, generative AI, and modern cloud‑based AI platforms while working under the guidance of senior engineers and technical leads. The Associate AI Engineer will contribute to AI application development, model integration, data preparation, testing, and deployment, while learning enterprise standards for security, governance, and responsible AI. ESSENTIAL DUTIES AND RESPONSIBILITIES Following is a summary of the essential functions for this job. Other duties may be performed, both major and minor, which are not mentioned below. Specific activities may change from time to time. Key Responsibilities AI & Application Development • Assist in building and enhancing AI‑driven applications using Python and modern AI/ML frameworks. • Support development ofLLM-powered applications(e.g., chatbots, copilots, intelligent workflows) using APIs and SDKs. • Implement prompt engineering, basic agent logic, and tool integrations under senior guidance. • Develop and maintain APIs or microservices (e.g., FastAPI) that expose AI capabilities. Data & Model Support • Help prepare, clean, and validate datasets used for training, evaluation, or retrieval‑augmented generation (RAG). • Assist with model evaluation, testing, and performance analysis. • Support integration of pre‑trained models or managed AI services rather than building models from scratch. Cloud & DevOps Collaboration • Work with cloud‑based AI platforms (e.g., Azure, AWS, or equivalent) to deploy and test AI solutions. • Follow CI/CD practices for AI applications using enterprise source control (e.g., GitLab). • Learn and adhere to deployment, configuration, and environment‑management standards. Governance, Security & Responsible AI • Follow enterprise AI governance, data privacy, and security guidelines. • Assist with documentation related to model usage, data sources, and system behavior. • Apply responsible AI principles, including transparency, fairness, and safe usage patterns. Collaboration & Learning • Collaborate with product managers, data scientists, designers, and senior engineers. • Participate in code reviews, design discussions, and team knowledge‑sharing sessions. • Actively learn new AI tools, frameworks, and best practices through hands‑on work and mentorship. Qualifications Required Qualifications The requirements listed below are representative of the knowledge, skill and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. 1. Bachelor’s degree in Computer Science, Data Science, AI, Software Engineering, or related field. 2. Basic understanding of AI models, data architectures, and analytics methodologies. Preferred Qualifications • Strong foundation inPython programming. • Basic understanding of:  • Machine learning concepts (supervised/unsupervised learning, evaluation metrics) • Generative AI and large language models (LLMs)and frameworks (Langchain/AWS Strands/Microsoft Agent Framework) • Familiarity with REST APIs and basic web services. • Experience with Git and collaborative software development workflows. • Strong problem‑solving skills and willingness to learn in a fast‑evolving AI landscape. • Internship, co‑op, or academic project experience in AI, ML, or data science. • Exposure to one or more AI/ML libraries (e.g.,PyTorch, TensorFlow, scikit‑learn). • Familiarity with:  • LLM APIs (e.g., Azure OpenAI, OpenAI, AWS Bedrock) • Prompt engineering or retrieval‑augmented generation (RAG) • FastAPI,Streamlit, or similar frameworks • Basic understanding of cloud services (Azure, AWS, or GCP). • Experience working in regulated or enterprise environments is a plus. • Industry recognized AI Engineer Certifications(Azure, AWS, or GCP)