As an AI/ML Engineer, you will develop cutting-edge applications and systems leveraging AI tools and Cloud AI services. You'll ensure production-ready quality through robust cloud or on-prem application pipelines. This role offers the opportunity to apply Generative AI models as core components of solutions, potentially including deep learning, neural networks, chatbots, and image processing.

This position requires a strong foundation in building and deploying AI-enabled applications and agentic workflows within dynamic enterprise and client environments. A proven track record of contributing significant code to production software, AI, ML, or data systems is essential; coursework, certifications, or prototype-only experience are not sufficient for this role.

You will be instrumental in building sophisticated AI services, agents, retrieval pipelines, and essential application integrations. Developing production-quality Python services and robust APIs will be a key focus.

Connecting AI systems to critical enterprise data sources and business applications is a core responsibility. You will implement comprehensive automated tests, evaluations, logging, tracing, and monitoring systems.

Troubleshooting model outputs, data quality issues, integration failures, and performance bottlenecks will be part of your daily tasks. You'll also provide support for deployment, user testing, and production stabilization efforts. Documenting reusable components and effective implementation patterns will contribute to team knowledge.

Practical experience utilizing tools like Cursor, GitHub Copilot, Claude Code, Codex, or Replit Agent for building and deploying AI services is required. This includes the ability to craft effective task specifications, validate generated code, create comprehensive tests, troubleshoot failures, and manage Git and CI/CD workflows safely.

A minimum of three years of professional experience in relevant areas is necessary. The educational requirement is 15 years of full-time education. Expertise in Python, APIs, data structures, software engineering principles, and testing methodologies is fundamental.