This role focuses on developing cutting-edge AI and ML applications, leveraging cloud AI services and ensuring production-ready quality through robust pipelines. You will integrate Generative AI (GenAI) models into solutions, potentially working with deep learning, neural networks, chatbots, and image processing.

The position requires a strong foundation in developing and deploying AI-enabled applications and agentic workflows within dynamic enterprise environments. Practical experience is paramount; coursework, certifications, and prototype-only contributions are not sufficient. You should possess hands-on experience using tools like Cursor, GitHub Copilot, Claude Code, Codex, or Replit Agent for building and deploying AI services, including crafting effective task specifications, validating code, creating tests, and managing Git/CI/CD workflows.

Key responsibilities include building robust AI services, agents, retrieval pipelines, and essential tools for application integrations. You will develop production-quality Python services and APIs, seamlessly connecting AI systems to enterprise data sources and business applications.

This role also involves implementing comprehensive automated testing, evaluations, logging, tracing, and monitoring. You will be tasked with troubleshooting model outputs, data quality issues, integration failures, and performance bottlenecks. Support for deployment, user testing, and production stabilization is also expected, alongside documenting reusable components and implementation patterns.

A minimum of three years of professional experience is mandatory for this position. Essential technical proficiencies include expertise in Python, APIs, data structures, software engineering principles, and testing methodologies. A deep understanding of LLMs, RAG, embeddings, vector databases, tool calling, and prompt design is critical.

Furthermore, familiarity with cloud services, Docker, Git, CI/CD pipelines, SQL, and backend frameworks is required. Candidates should also be mindful of secure coding practices, observability, latency, and cost considerations in their development work.