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

Description Job Description

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.

Responsibilities End-to-End System Responsibility:

• 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.

Technical Leadership

• 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.

AI Agent Design And Problem-Solving

• 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.

Research And Development

• 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.

Collaboration And Communication

• 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.

Additional Responsibilities

• 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…