Machine Learning Engineer / AI Engineer

Day to Day job Duties: (what this person will do on a daily/weekly basis)
• Partner with an assigned CIO organization as the primary GenAI engineering lead and translate business priorities into scalable AI solutions.
• Design, develop, test, and deploy production-grade GenAI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agentic AI patterns.
• Accelerate high-value use cases through hands-on engineering, rapid prototyping, reusable components, reference architectures, and production solutioning.
• Drive adoption of enterprise AI platforms such as Azure AI, AWS Bedrock, and internal platforms; translate platform capabilities into practical implementation patterns.
• Design secure RAG and agentic workflows using document ingestion, embeddings, vector search, orchestration frameworks, tools/APIs, enterprise data sources, and guardrails.
• Ensure solutions meet enterprise architecture, security, privacy, responsible AI, governance, observability, and production support standards.
• Deliver workshops, training, design reviews, and technical guidance on prompt engineering, RAG, orchestration, LLM application patterns, and platform usage.
• Develop reusable documentation, playbooks, quick-start guides, sample code, and best practices while acting as liaison between centralized AI platform and CIO engineering teams.

Basic Qualifications:
• Minimum 8+ years of overall software engineering, data engineering, machine learning engineering, or AI solution development experience in enterprise environments.
• Minimum 5+ years of hands-on experience designing and delivering machine learning, AI, or data-driven applications using Python and modern software engineering practices.
• Minimum 3+ years of hands-on experience building Generative AI / LLM applications, including prompt engineering, model integration, evaluation, and production deployment.
• Minimum 3+ years of experience designing and implementing RAG solutions using embeddings, vector search/databases, document processing, retrieval techniques, and grounding patterns.
• Minimum 2+ years of experience developing agentic AI or multi-step AI workflows using orchestration frameworks, tool/function calling, APIs, and enterprise integrations.
• Minimum 3+ years of experience with cloud AI/ML platforms, including Azure AI / Azure OpenAI, AWS Bedrock, or comparable enterprise AI services.
• Minimum 3+ years of experience with cloud-native engineering, CI/CD, containers, observability, security controls, and production deployment practices.
• Minimum 2+ years of experience in technical enablement, architecture, consulting, or engineering leadership, including workshops, mentoring, reusable guidance, and application of responsible AI/governance requirements.
Travel: Travel as required based on business and CIO-aligned engagement needs.
Degree: Bachelor's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, Machine Learning, or equivalent work experience.

Nice to Have; (But not a must)
• Experience with LangChain, LangGraph, Semantic Kernel, LlamaIndex, AutoGen, or comparable LLM / agent orchestration frameworks.
• Experience with vector technologies such as Azure AI Search, OpenSearch, Pinecone, pgvector, FAISS, or equivalent platforms.
• Experience with LLMOps / MLOps, prompt and model evaluation, AI observability, guardrails, red-teaming, and production quality measurement.
• Experience building reusable GenAI accelerators/reference architectures or delivering AI solutions in highly regulated enterprise environments such as financial services.