AI Engineer

Work Model: You’ll work in a hybrid model, onsite at your designated Regal Rexnord location, with flexibility to work remotely. ____________________ Roles and Responsibilities: Design, develop, and maintain full-stack applications using Python, FastAPI, React, JavaScript/TypeScript, REST APIs, and modern web development practices. Build GenAI-enabled application features using large language models (LLMs), prompt engineering, RAG pipelines, embeddings, vector search, and agentic AI patterns. Develop internal AI assistants, chatbots, workflow automation tools, intelligent dashboards, and productivity applications to improve engineering and business workflows. Implement backend services, API integrations, data processing logic, authentication support, error handling, logging, and application-level validations. Build and enhance RAG pipelines using internal documents, product documentation, user guides, technical specifications, requirements, and structured or unstructured enterprise data sources. Work with AI platforms and LLM APIs such as Azure AI Foundry, Azure OpenAI, OpenAI-compatible APIs, AWS Bedrock, or similar enterprise AI platforms. Use AI-assisted development tools such as Cursor AI, GitHub Copilot, Claude Code, and GitHub SpecKit to improve productivity, code quality, and SDLC discipline. Apply specification-driven development practices by contributing to specifications, implementation plans, task breakdowns, acceptance criteria, and technical documentation. Collaborate with product owners, business analysts, architects, engineering managers, and senior engineers to convert business needs into working software features. Participate in code reviews, debugging, unit testing, integration testing, defect fixing, CI/CD activities, and release readiness reviews. Support Docker-based deployment, environment configuration, cloud deployment basics, and operational troubleshooting for developed applications. Follow secure engineering and responsible AI practices, including data privacy, access control, prompt safety, output validation, and safe handling of enterprise information. Continuously learn and evaluate emerging GenAI frameworks, AI coding tools, agentic AI patterns, and modern software engineering practices.

Education: Bachelor’s degree in Computer Science, Information Technology, Data Science, Artificial Intelligence, Engineering, or a related technical field.

0–2 years of professional software development experience, internship experience, or equivalent hands-on project experience in full-stack, backend, or GenAI application development. Technical Exposure: Hands-on experience with Python, REST APIs, web application development, Git/GitHub, and AI-assisted development tools such as Cursor AI, GitHub Copilot, or similar tools. GenAI Exposure: Practical understanding of LLMs, prompt engineering, RAG, embeddings, AI APIs, or agentic workflow concepts. Development Practices: Familiarity with agile development, SDLC, code reviews, testing, documentation, and incremental delivery of software features.

Strong programming skills in Python and basic understanding of JavaScript/TypeScript, React, HTML, and CSS. Hands-on understanding of backend development using FastAPI, Flask, Django, or similar Python frameworks. Ability to build REST APIs, integrate external APIs, process JSON data, and troubleshoot application issues. Basic understanding of databases such as PostgreSQL, SQL Server, SQLite, DuckDB, MongoDB, or vector databases such as pgvector, FAISS, Chroma, Pinecone, or Azure AI Search. Exposure to GenAI frameworks such as LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, LlamaIndex, Haystack, Agno, or similar frameworks is preferred. Understanding of Docker, CI/CD basics, cloud deployment basics, and application configuration management is preferred. Ability to understand requirements, break down work into tasks, and deliver working software in collaboration with the