Applied AI/ML Engineer

About This Opportunity

We are representing a confidential technology firm that builds impactful software solutions for the federal sector. Our client is seeking an Applied AI/ML Engineer to design, build, and deploy practical AI-powered capabilities into production systems. This is a hands-on role focused on applying modern machine learning and generative AI techniques to solve real-world problems—from experimentation and prototyping through deployment, evaluation, and continuous iteration.

You'll collaborate closely with cross-functional teams to transform ambiguous challenges into scalable, reliable AI-driven features. This role balances technical rigor with practical constraints and offers the opportunity to make a meaningful impact on systems used by millions.

Responsibilities

• Design, develop, and deploy machine learning and AI-powered features into production systems
• Apply supervised, unsupervised, and deep learning techniques to structured and unstructured data
• Build and evaluate models for classification, ranking, prediction, natural language processing, and anomaly detection
• Develop and integrate generative AI solutions, including LLM-based workflows, retrieval-augmented generation, and agent systems
• Translate business and user requirements into well-defined ML problem statements, metrics, and experiments
• Implement data pipelines and feature engineering workflows to support model training and inference
• Evaluate model performance, bias, drift, and reliability; iterate based on findings
• Collaborate with software engineers to integrate models into APIs, services, and user-facing applications
• Contribute to architecture decisions around model serving, scalability, and cost optimization
• Document approaches, assumptions, and tradeoffs to support maintainability and knowledge sharing

What We're Looking For

Required qualifications:

• Strong foundation in machine learning concepts, including model selection, training, validation, and evaluation
• Proven experience building and deploying ML models in real-world applications
• Proficiency in Python and common ML libraries such as PyTorch, TensorFlow, and scikit-learn
• Experience working with large language models, embeddings, and prompt-driven systems
• Familiarity with data processing tools and workflows (Pandas, SQL, Spark, or similar)
• Understanding of software engineering best practices including version control, testing, and code reviews
• Ability to reason about tradeoffs between accuracy, latency, cost, and maintainability
• Strong communication skills and comfort working in cross-functional teams
• Authorization to work in the United States; certain roles may require U.S. citizenship and the ability to obtain and maintain a federal background investigation or security clearance
• Must be located in or willing to relocate to the DMV area (DC, Maryland, Virginia) for hybrid collaboration

Bonus qualifications:

• Experience in innovation, R&D, labs, or exploratory engineering environments
• Experience deploying models to cloud platforms and managing inference at scale
• Familiarity with MLOps practices such as model monitoring, CI/CD for ML, and experiment tracking
• Experience contributing to architectural discussions or technical strategy

How We Work With You

Our talent team at Get A Job.ai partners with exceptional organizations to connect them with top-tier candidates. When you apply through our platform, a dedicated recruiter will review your profile and conduct an initial screening. If there's a strong match, we'll submit your candidacy to our client for consideration. Please apply exclusively through Get A Job.ai—direct contact with the client is not part of this process.

Pay

Compensation details will be discussed during the interview process and are determined by job category, location, and candidate skill level.

Location: Hybrid/Remote (DMV area: DC, Maryland, Virginia)

Employment Type: Full-time

Equal Opportunity: Get A Job.ai is committed to equal employment opportunity and does not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability, or any other protected characteristic.