Capital Technology Group provides expert consulting services software development, digital transformation, human-centered design, data analytics and visualization, and cybersecurity.

Our multidisciplinary teams use agile methodologies to rapidly and incrementally deliver value in close collaboration with our clients. For over a decade, we have been trusted by both federal and commercial clients to solve complex, mission-critical business challenges. The quality of our work has been recognized by our partners and peers through our inclusion in the Digital Services Coalition, a group of forward- thinking firms recognized for excellence in delivering IT services.

Description

Capital Technology Group (CTG) is on a mission to modernize and innovate the way the federal government delivers software. We are passionate about our work, dedicated to our clients, and committed to a culture of continuous learning and growth. For this role specifically, we are seeking individuals to help support high-impact, civic tech within the federal government.

As an integral part of the program, the AI/ML Engineer leads the team in evaluating new or emerging technologies using prototypes and/or proof of concepts, analyzes and communicates the benefits and risks in implementing solutions using the new technologies, and lead teams to support the adoption of new technologies across the enterprise. The AI/ML Engineer will also provide technical leadership by supervising and mentoring junior IT specialists.

Client Requirements: applicants MUST BE US Citizens and be able to obtain Public Trust clearance

Responsibilities

Leads the team in evaluating new or emerging technologies using prototypes and/or proof of concepts
Analyzes and communicates the benefits and risks in implementing solutions using the new technologies
Shall have the ability to perform as a developer, operations, and security engineer in cloud environments
Lead teams to support the adoption of new technologies across the enterprise
Provides technical leadership by supervising and mentoring junior IT specialists

Requirements

Bachelors Degree
15 years of experience
Proficiency in Python, machine learning frameworks (like TensorFlow or PyTorch), data manipulation using pandas, and experience with cloud platforms (AWS, Google Cloud, or Azure)
Must be able to get up-to-speed quickly on new projects and technologies
Strong communication and problem-solving skills

Nice to Have Skills

People Management Experience

Benefits

Remote Work (Hybrid roles will be specified in the job post)
Competitive Compensation Package
Medical, Dental, and Vision
Life Insurance, Short/Long Term Disability
Employee Assistance Program
401(k) with 4% matching
Liberal PTO vacation policy
Generous Annual Continuing Education
Annual Wellness Budget
Bonus Incentive Programs (Employee referrals and performance-based rewards)

Thanks for your interest in Capital Technology Group!

Capital Technology Group is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law.

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Are you a U.S. Citizen currently residing in the U.S.? *

Client Requirements: applicants must be a US Citizen

I understand that if hired, I must successfully complete a DHS background check. *

Client Requirements: applicants must be able to obtain Public Trust clearance

Where will you be working from? (City, State) *

What initially sparked your interest in this role? *

How many years of relevant experience do you have? *

Based on your experience, what are the most important characteristics of a high performing team? *

Please provide your salary requirements or range. *

Please provide two references and contact information. *

Please provide a link to your LinkedIn profile. *

Were you referred by a CTG employee? If so, please provide their name below. *

Describe a time when you deployed a machine learning model into production. What technologies did you use? What were the key challenges, and how did you address them? *

Can you describe a situation where your model's predictions were significantly off from what was expected? How did you detect, diagnose, and resolve the issue? *

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