AI Engineer

Overview

Join the Data & AI engineering team as an AI Engineer building GenAI applications, full-stack Python applications, internal productivity tools, and AI-enabled workflow applications. The role covers the full software development lifecycle, from requirements understanding and solution design through coding, testing, documentation, deployment support, and continuous improvement.

What you'll do

• 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, prompt engineering, RAG pipelines, embeddings, vector search, and agentic AI patterns.
• Develop internal AI assistants, chatbots, workflow automation tools, intelligent dashboards, and productivity applications.
• 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.
• Contribute 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.
• 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.

What you'll need

• 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.
• 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.
• Practical understanding of LLMs, prompt engineering, RAG, embeddings, AI APIs, or agentic workflow concepts.
• 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.
• 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.

Nice to have

• Exposure to GenAI frameworks such as LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, LlamaIndex, Haystack, Agno, or similar frameworks.
• Understanding of Docker, CI/CD basics, cloud deployment basics, and application configuration management.
• Awareness of responsible AI practices such as hallucination control, prompt injection risk, output validation, privacy, security, and access control.

Details

• Location: Hyderabad, Telangana, India.
• Work in a hybrid model, onsite at the designated Regal Rexnord location, with flexibility to work remotely.