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.

Qualifications:

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

• Experience: 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.

Skills:

• 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 team.

• Good communication, documentation, analytical thinking, ownership mindset, and willingness to learn new technologies.

• Awareness of responsible AI practices such as hallucination control, prompt injection risk, output validation, privacy, security, and access control is a plus.

Preferred / Good to Have:

• Experience building GenAI applications such as chat assistants, document Q&A systems, summarization tools, workflow assistants, or intelligent automation tools.

• Exposure to GitHub SpecKit commands and artifacts such as /speckit.constitution, /speckit.specify, /speckit.plan, and /speckit.tasks.

• Experience with Azure AI Foundry, Azure OpenAI, OpenAI APIs, AWS Bedrock, or OpenAI-compatible model endpoints.

• Understanding of LLMOps or MLOps concepts such as prompt versioning, evaluation, observability, latency, token cost, monitoring, and feedback loops.

• Ability to explain technical work clearly through documentation, demos, diagrams, and status updates

About Regal Rexnord