A Machine Learning Engineer specializes in designing, building, and deploying machine learning models. They utilize statistical and mathematical techniques, parallelizing processing, hyperparameter tuning, and other optimization methodologies to improve model performance. Responsibilities also include collecting and preprocessing large datasets, conducting exploratory data analysis, working closely with data engineers to understand data requirements, and engineer input variables for machine learning models.
Health, Dental, Vision, 401K, PTO, Strong employer contribution to retirement Key Responsibilities Design, develop, deploy, and support AI-powered applications, copilots, agents, and workflow automations. Build production-ready AI solutions using LLMs, RAG, vector databases, embeddings, semantic search, and prompt engineering. Develop AI agents that securely leverage enterprise data, knowledge repositories, APIs, and business workflows. Integrate AI solutions with cloud platforms, enterprise systems, and collaboration tools across Azure and AWS. Create reusable AI components, prompts, connectors, and frameworks to accelerate future AI development. Partner with business and technical stakeholders to translate use cases into scalable AI products with measurable business impact. Ensure AI solutions meet enterprise standards for security, governance, privacy, and responsible AI. Required Qualifications 7+ years of software engineering experience, including 2+ years building enterprise AI solutions.
Hands-on experience with:
LLM application development RAG architectures Prompt engineering Embeddings and vector search LLM APIs and model evaluation Agentic AI and workflow orchestration Strong Python development skills and hands-on experience with AI/ML technologies. Experience building LLM-based applications using RAG, prompt engineering, embeddings, vector search, and agentic AI frameworks. Proven success delivering production-ready AI solutions that drive automation, productivity, or business efficiency. Experience deploying and supporting applications in Azure and/or AWS environments. Preferred Qualifications Experience with Azure AI Foundry, Azure AI Search, Copilot Studio, Amazon Bedrock, SageMaker, Pinecone, OpenSearch, or similar AI platforms. Knowledge of NLP, semantic search, vector databases, and model evaluation. Experience integrating AI solutions through APIs, containers, Azure Functions, or AWS Lambda. Familiarity with AI governance, security, and responsible AI best practices. #
