This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Lead AI/ML Engineer based in the United States.
This is a senior technical leadership opportunity focused on designing and deploying advanced AI and machine learning solutions for high-impact federal initiatives.
You will evaluate emerging technologies, turn promising concepts into production-ready applications, and help accelerate enterprise AI adoption.
The role combines hands-on engineering with technical strategy, cloud architecture, experimentation, and client engagement.
You will work extensively in AWS environments while applying modern ML frameworks, platforms, and MLOps practices.
The position also offers the opportunity to mentor engineering talent and influence technical standards across multidisciplinary teams.
You will regularly translate complex technical findings into clear recommendations for engineering leaders, executives, and federal clients.
This role is well suited to an experienced AI/ML leader who enjoys innovation, mission-driven work, and solving complex problems at scale.
Accountabilities:
- Lead technical evaluations of emerging AI and machine learning technologies through prototypes, proof-of-concepts, experimentation, and comparative assessments.
- Analyze the benefits, limitations, tradeoffs, implementation considerations, and risks of emerging technologies and communicate findings to technical and business stakeholders.
- Design, develop, test, and deploy production-ready AI/ML applications within AWS cloud environments.
- Build, train, evaluate, and optimize machine learning models for enterprise and mission-critical use cases.
- Support AI/ML application development across engineering, operations, security, and other cloud-native disciplines.
- Drive adoption of modern AI technologies, engineering practices, and scalable approaches across enterprise environments.
- Collaborate directly with federal clients and multidisciplinary engineering teams to understand requirements and deliver high-impact technical solutions.
- Present technical findings, experiment results, architecture recommendations, and implementation strategies to clients, executive stakeholders, and internal leadership.
- Provide technical leadership and mentorship to software and machine learning engineers, helping strengthen engineering capabilities and professional development.
- Contribute across multiple projects, rapidly assessing new technologies and determining how they can be applied to solve real-world challenges.
- Support the development and adoption of MLOps, CI/CD, containerization, DevSecOps, and secure software engineering practices where appropriate.
Requirements
- Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, Information Systems, or a related technical discipline, or equivalent professional experience.
- 7–10 years of professional experience in software engineering, cloud engineering, or a closely related technical field.
- At least 3 years of experience deploying production enterprise applications in AWS environments.
- At least 2 years of experience deploying production AI/ML applications within enterprise environments.
- Strong Python programming skills and hands-on experience with machine learning frameworks such as TensorFlow or PyTorch.
- Experience with AI/ML platforms and services such as Amazon Bedrock, SageMaker, Hugging Face, Weights & Biases, or comparable technologies.
- Strong experience with data analysis and manipulation using pandas, as well as experiment visualization and presentation using tools such as matplotlib.
- Strong analytical, problem-solving, and technical communication skills, with the ability to explain complex AI/ML concepts to both technical and executive audiences.
- Ability to quickly learn emerging technologies and contribute effectively across multiple projects and technical domains.
- Experience leading, managing, or mentoring engineering or ML teams is highly desirable.
- Experience with LLMs, generative AI, retrieval-augmented generation (RAG), AI agents, MLOps, Docker, Kubernetes, CI/CD, DevSecOps, or secure software development is a strong advantage.
- Experience supporting federal, regulated, or mission-critical environments is beneficial.
- Applicants must be U.S. Citizens and able to obtain a Public Trust clearance.
Benefits
- Annual salary range of $180,000–$230,000 USD, with final compensation based on experience, education, skills, and other relevant factors.
- Eligibility for an annual discretionary bonus and performance-based incentive opportunities.
- Remote work environment, with hybrid arrangements specified where applicable.
- Medical, dental, and vision insurance.
- Life insurance and short- and long-term disability coverage.
- Employee Assistance Program.
- 401(k) plan with 4% company matching.
- Liberal paid time off and vacation policy.
- Generous annual continuing education support.
- Annual wellness budget.
- Employee referral and performance-based bonus programs.
- Opportunity to work on meaningful AI/ML initiatives supporting high-impact federal missions.
- Collaborative environment with opportunities for technical leadership, mentorship, and professional growth.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
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