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Remote role, mid-level (3–5 years), and broad GenAI skillset drive high applicant competition.
Core ML/LLM engineering skills are transferable, though enterprise GenAI practices add moderate domain specificity.
Mandatory cloud AI certification plus explicit 3–5 years and specific GenAI tooling increases filter strictness.
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End-to-end ownership for building, deploying, and maintaining AI/ML and Generative AI applications including data pipelines, model workflows, APIs, evaluation, monitoring, and continuous improvement.
Design and implement Retrieval-Augmented Generation (RAG) and agentic AI workflows integrating foundation models and AI services, ensuring secure and responsible AI development.
Collaborate across teams to translate requirements into reliable, production-grade solutions; contribute to CI/CD, containerization, cloud deployment, documentation, and code quality activities.
3–5 years professional experience in software or machine learning development, with hands-on expertise in Generative AI or LLM-based applications.
Bachelor's or Master's degree in CS, Data Science, AI/ML, Engineering, or equivalent practical experience.
Strong Python programming skills; experience with ML libraries (e.g., PyTorch, TensorFlow); working knowledge of LLM application patterns like prompting, embeddings, RAG, vector DBs, and agent workflows.
Mandatory current AWS AI/ML or Microsoft Azure AI certification; experience with cloud platforms (AWS, Azure, or GCP), Docker containerization, REST API development, Git/GitHub, and CI/CD workflows.
Proven ability to deliver enterprise-grade AI/ML solutions, integrating foundation models and AI services under constraints like latency, cost, privacy, and reliability.
Experience designing or implementing advanced AI patterns including RAG, agent workflows, prompt engineering, and secure AI practices.
Comfortable working cross-functionally with product, data science, and engineering teams to produce maintainable, testable, and monitored AI applications ready for production deployment.