Lead AI Engineer - Data Science and Algorithms

Maximus

5d•$180,000 - $200,000•Remote

About The Position

Maximus is currently seeking a Lead AI Engineer - Data Science and Algorithms. We have an exciting opportunity for a Lead AI Engineer to join the Maximus AI Transformation and Experience team, building and refining AI capabilities and algorithms used across the enterprise and the government programs we support. We are looking for an experienced AI engineer and Data Scientist who has built real algorithms with real data on real systems, shipped them to production, and learned what it takes to keep them running there. This is a hands-on individual contributor role where you will spend most of your time designing, building, testing, deploying, and improving algorithms. AI and machine learning are important parts of the solutions we build and we’re looking for someone who enjoys right sizing algorithms for the problems at hand while squeezing every last ounce of performance out of our solutions. You will work alongside talented engineers and technical specialists on challenging problems where experimentation, engineering judgment, and pragmatic decision-making matter. The work has meaningful impact, improving how Maximus employees do their jobs and the experiences of the citizens who rely on the government services we help deliver. This is a remote position.

Requirements

Bachelor's degree in a relevant field of study and 10+ years of relevant professional experience required.
7+ years of professional advanced algorithm building and data science experience, including substantial experience building and supporting production algorithms on systems processing real data.
7+ years of professional Data Science or advanced algorithm building experience.
5+ years of experience designing or building solutions in cloud environments.
3+ years of experience with artificial intelligence, machine learning, or related technologies.
Demonstrated experience developing algorithms including AI and machine learning algorithms.
Strong understanding of the application of mathematics, AI, and signal processing in production algorithms.
Experience designing and implementing algorithms on AWS, Azure, GCP, or similar cloud platforms.
Experience with deploying algorithms on production systems outside of notebooks (e.g. outside sagemaker, jupyter).
Experience building or integrating AI/ML capabilities into software systems.
Ability to communicate technical decisions, tradeoffs, and risks clearly with technical and non-technical colleagues.
US Citizen and able to obtain a Public Trust.

Nice To Haves

Deep expertise in Python, demonstrated through substantial production software rather than scripts, notebooks, or prototypes alone.
Proficiency in at least one systems programming language, such as Rust, Go, C++, or C, with experience applying it to real-world software engineering problems.
Experience building and operating distributed or cloud-native production systems.
Experience building algorithms with classical signal processing.
Experience building AI models from scratch
A track record of taking algorithms from initial development through production and ongoing operation.
Experience diagnosing complex production problems across application, infrastructure, data, and integration boundaries.
Practical experience building algorithmic solutions leveraging large language models, machine learning models, or managed AI services.
Experience working in environments where security, privacy, reliability, or regulatory requirements influence engineering decisions.
Experience improving existing systems where prior decisions limit your options.
Experience mentoring staff through collaboration, design reviews, and technical problem solving.

Responsibilities

Lead, develop, collaborate, and advance the applied and responsible use of AI, ML, mathematical, and data science solutions throughout the enterprise by finding the right fit of tools, technologies, methodologies, processes, and automation to enable effective and efficient solutions for each unique situation. Lead the use of applied mathematical analyses to provide solutions.
Lead efforts across the enterprise to support the creation of solutions and real mission outcomes, emphasizing and teaching the ability to flex and demonstrate initiative when dealing with ambiguous and fast-paced situations.
Act as technical translator and role model for effectively articulating and translating technical needs, solutions, outputs, and impacts to all levels, regardless of technical proficiency, in a respectful, collaborative, and situationally appropriate manner.
Maintain deep, current knowledge of the AI technology landscape and emerging developments, evaluating their applicability for use in production/operational environments.
Lead the creation, curation, and promotion of playbooks, best practices, lessons learned, and firm intellectual capital.
Design, build, and improve production AI solutions.
Own difficult technical problems from initial exploration through implementation, deployment, troubleshooting, and continuous improvement.
Write high-quality software and contribute directly to custom enterprise capabilities.
Build AI-enabled applications that integrate models, enterprise data, cloud services, APIs, and existing systems.
Improve AI solutions by incorporating holistic algorithmic improvements.
Evaluate emerging AI technologies through hands-on experimentation and determine where they can provide practical value in production.
Build reusable components, libraries, patterns, and reference implementations that enable other teams to deliver AI-based capabilities.
Mentor other staff through solutioning of custom algorithms and solution enhancements.