Machine Learning Engineer III - AI Agent Engineer - Digital and Technology Partners - Onsite/Hybrid

This position is Onsite/Hybrid - Requires 1 day onsite a week

Location: 150 E 42nd Street, New York, NY

Machine Learning Engineer III - AI Agent Engineer will oversee the design and development of sophisticated machine learning systems. They will collaborate closely with cross-functional teams to drive innovation and ensure the successful implementation of machine learning solutions.

• →Assume full ownership of the design, development, deployment, governance, and continuous evolution of AI agent ecosystems and autonomous workflows.
• →Architect and deliver end-to-end agentic AI solutions leveraging Large Language Models (LLMs), multi-agent systems, Retrieval-Augmented Generation (RAG), orchestration frameworks, and enterprise integrations.
• →Lead the collaborative efforts with cross-functional teams, including data scientists and product managers, to ensure the successful deployment and robust maintenance of machine learning models.
• →Oversee the continuous monitoring and timely updating of deployed models to guarantee enduring performance and reliability.
   

• →Develop and maintain project work plans, including critical tasks, milestones, timelines, interdependencies, and contingencies.  Tracks and reports progress.  Keeps stakeholders apprised of project status and implications for completion. 
• →Prepare clear, well-organized project-specific documentation, including, at a minimum, analytic methods used, key decision points and caveats, with sufficient detail to support comprehension and replication. 
• →Mentor other analysts on how to a) determine appropriate statistical analysis methods, b) leverage appropriate programing languages and tools, and c) generate and interpret statistical analysis outputs.
• →Share development and process knowledge with other analysts in order to assure redundancy and continuously builds a core of analytical strength within the organization.
• →Adheres to corporate standards for performance metrics, data collection, data integrity, query design, and reporting format to ensure high quality, meaningful analytic output.
• →Works closely with IT on the ongoing improvement of Mount Sinai’s integrated data warehouse, driven by strategic and business needs, and designed to ensure data and reporting consistency throughout the organization.
• →Demonstrates advanced level proficiency with the principles and methodologies of process improvement. Applies these in the execution of responsibilities in support of a process focused approach.
• →Other duties as assigned.

• →Bachelor’s degree in Computer Science, Data Science, or a related field.
• →4+ years of relevant experience in machine learning and back-end software development.
• →1+ years of hands-on experience building and deploying Generative AI, LLM, RAG, Copilot, or Agentic AI solutions in production environments.
• →Experience with LLM platforms and frameworks such as Azure AI Foundry, Azure OpenAI, OpenAI, Anthropic, LangChain, LangGraph, Semantic Kernel, CrewAI, AutoGen, or similar technologies.
• →Experience building RAG architectures utilizing vector databases such as Pinecone, Azure AI Search, Elasticsearch, Weaviate, Chroma, or equivalent platforms.
  Demonstrated end-to-end machine learning system development and operation experience, covering the complete Software Development Life Cycle (SDLC).
• →Proficiency in multiple programming languages and machine learning frameworks and tools.
• →Solid experience with both SQL and NoSQL databases.
• →Extensive experience with Big Data technologies like Apache Spark.
• →Hands-on experience in Unix environments.
• →Practical knowledge and experience with at least one cloud system among AWS, Azure, or Google Cloud.
• →Familiarity with continuous development and integration systems such as Jenkins, Git, Azure DevOps, and Terraform.
• →A proven history in developing, deploying, and operating efficient and reliable machine learning systems.
• →Strong leadership and effective communication skills to facilitate cross-functional collaboration throughout the organization.
• →Experience in providing mentorship

• Exhibit technical leadership and mentorship to Machine Learning Engineer I, II, and other team members.
• Encourage knowledge exchange and promote professional growth within the team.
• Establish and enforce best practices for machine learning system development, including coding standards, code reviews, and comprehensive documentation.
• Serve as the primary contact for technical inquiries related to machine learning within the team, providing expert guidance and solutions.

• Design, implement, and optimize intelligent agents capable of planning, reasoning, decision-making, workflow automation, and task execution.
• Develop agent frameworks that combine structured workflows, tool usage, knowledge retrieval, and autonomous decision-making.
• Solve complex technical and business challenges through innovative application of generative AI, machine learning, and software engineering principles.
• Drive root-cause analysis and resolution of agent performance issues, hallucinations, model drift, workflow failures, and integration challenges.
• Foster collaborative problem-solving across technical and business teams to deliver scalable AI-driven solutions.
   

• Remain up-to-date with the latest advancements and trends in the fields of AI agents, Generative AI, software engineering and machine learning.
• Conduct proactive research to uncover new methods and techniques applicable to future projects.
• Lead initiatives to explore, test, and implement new technologies and frameworks, ensuring the team is always at the forefront of industry innovations.

• Engage in effective communication and collaboration across various teams within the organization to ensure alignment and coordination in the execution of machine learning projects.
• Facilitate transparent and timely communication with all relevant stakeholders, ensuring that all are kept informed of project statuses, challenges, and achievements.
• Work collaboratively with cross-functional teams, fostering a cooperative environment for the seamless integration of machine learning systems into diverse organizational processes and workflows.
• Contribute to building strong interdepartmental relationships, enabling the efficient exchange of knowledge and expertise, and ensuring the successful realization of machine learning initiatives.