Senior AI Developer / Technical Lead

Guidehouse•Atlanta, GA

4d•$118,000 - $196,000

About The Position

The Senior AI Developer / Technical Lead leads the design, development, deployment, and delivery of complex artificial intelligence, machine learning, generative AI, and public health analytics solutions for the Centers for Disease Control and Prevention and related clients. The role combines hands-on technical leadership with end-to-end project oversight, translating program objectives into scalable AI architectures and actionable delivery plans. It guides multidisciplinary teams, establishes secure and responsible AI development practices, manages technical risks, and ensures high-quality, compliant solutions. The role serves as a primary liaison between clients, product stakeholders, and technical teams while driving innovation, reproducibility, maintainability, and continuous improvement across AI and analytics workflows.

Requirements

U.S. citizenship or Green Card is contractually required.
Must be able to obtain and maintain a Federal or DHS Public Trust.
Bachelor's degree.
Minimum of five years of professional experience.
Experience in data engineering, software development, or related roles.
Proven ability to lead multidisciplinary technical teams and communicate complex AI concepts, architecture decisions, risks, and recommendations to technical and non-technical stakeholders in public health, healthcare, government, or other regulated environments.

Nice To Haves

Senior-level experience designing and developing AI or machine learning solutions using Python and modern AI/ML frameworks, including producing, reviewing, and troubleshooting production-quality code.
Experience supporting federal programs and/or data modernization projects.
At least three years of experience architecting and deploying AI applications in cloud environments, integrating models with enterprise data sources and APIs, and applying software engineering, DevSecOps, MLOps, testing, monitoring, and documentation practices.
At least five years of experience leading technical development teams.
Ability to work in a matrixed environment.
Experience delivering generative AI solutions using large language models, retrieval-augmented generation, prompt engineering, vector databases, semantic search, AI agents, and model evaluation techniques.
Experience with Azure AI services or comparable cloud AI platforms, containerization and orchestration, infrastructure automation, data pipelines, and federal security and compliance frameworks.
Familiarity with responsible AI, explainability, bias assessment, human-in-the-loop review, and model risk management.
Experience with Databricks, Snowflake, and other data processing or analytic technologies.

Responsibilities

Lead end-to-end planning, architecture, development, testing, deployment, and delivery of complex AI, machine learning, generative AI, and public health analytics solutions, ensuring scope, schedule, budget, security, performance, and quality objectives are met.
Translate client, user, and program objectives into technical requirements, AI solution architectures, product roadmaps, work plans, milestones, and deliverables aligned with federal standards, responsible AI principles, and stakeholder expectations.
Provide hands-on technical leadership to multidisciplinary teams of AI developers, machine learning engineers, data engineers, data scientists, biostatisticians, and epidemiologists, fostering collaboration, accountability, code quality, and high performance.
Serve as the primary technical point of contact for clients and stakeholders, communicating AI capabilities, limitations, tradeoffs, delivery status, and risks while facilitating timely technical and product decisions.
Design and develop production-ready AI solutions, including machine learning models, generative AI applications, retrieval-augmented generation workflows, intelligent agents, APIs, and reusable software components using modern development frameworks and cloud services.
Establish and oversee software engineering and MLOps practices, including version control, peer review, automated testing, CI/CD, model evaluation, monitoring, performance optimization, documentation, and secure deployment across development, test, and production environments.
Evaluate emerging AI technologies and select appropriate models, frameworks, platforms, and integration patterns based on solution requirements, cost, scalability, security, explainability, and operational sustainability.
Implement responsible AI, privacy, security, and data governance controls throughout the solution lifecycle; identify technical risks and dependencies; and mentor developers through architecture reviews, coding standards, troubleshooting, and knowledge sharing.