AI/ML Data Engineer, (Consultant, Data & Analytics)

Job Requirements We are seeking an AI/ML Engineer as part of our Consultant, Data & Analytics career path to build and operate production-grade AI and machine learning capabilities for a large federal agency’s enterprise data modernization initiative. This role supports a cloud-native data platform that enables secure, scalable, and data-driven decision-making across the agency through platform engineering, data governance, and AI/ML enablement. Rather than supporting a single application, this work builds the shared technology foundation that delivers reusable AI solutions, model monitoring, and responsible AI practices to customers across the enterprise. This is a fully remote role.

At Fearless , the consultant is responsible for leveraging data to answer business questions, develop algorithms and models, and build scalable infrastructure for managing and processing data. This role will possess and/or develop the skills to be able to span the domains of data analysts, engineers, and scientists at a practical application level of proficiency. This Consultant role will work on projects that allow them to design solutions that support decision-making, optimize operations, and enable data-driven innovation.

Your Responsibilities In This Role

• Build and deploy AI/ML capabilities using Azure Machine Learning, Azure AI Services, and Databricks to productionize models across the enterprise data platform.
• Implement MLOps practices to automate model training, validation, deployment, and monitoring workflows.
• Develop reusable AI solution patterns and templates that enable predictive modeling, natural language processing, and generative AI use cases.
• Integrate AI/ML capabilities with shared data engineering, platform, and governance services to ensure scalable, reusable enterprise solutions.
• Implement model monitoring, guardrails, and explainability features to ensure AI solutions comply with privacy, safety, fairness, and Trustworthy AI requirements.
• Support AI pilot initiatives, customer onboarding, and enablement by providing guidance on AI solution design, model deployment, and responsible AI practices.
• Coordinate with platform engineering teams to ensure AI solutions leverage shared infrastructure and comply with enterprise governance standards.
• Collaborate with engineers, data scientists, governance teams, and business stakeholders to identify AI/ML opportunities, support pilot implementations, and deliver production-ready AI solutions.

Fearless Impact The Impact You Will Create in this Role:

• Collaborates with cross functional teams
• Earn the trust of your customer and teammates by delivering world class services.
• Be reliable completing assigned objectives on time.
• Alert your teammates and supervisor immediately when you think a deliverable is at risk.
• Gather intelligence and listen to what your client, other people within the customer’s organization and other contractors say about your project, and report back to your Fearless teammates and supervisor (whether it’s good, bad or just informational).

Fearless Customer Impact

• Deliver expected outcomes relative to data that enable smarter, faster, and more informed customer decisions
• Offer and implement innovative solutions that introduce advanced analytics, machine learning, and emerging technologies for greater data accuracy and insight
• Serve as a trusted advisor in your area of expertise

Work Experience PROJECT SKILL AND EXPERIENCE REQUIREMENTS

• Minimum of 4 years of experience in AI/ML engineering, data science, or related roles.
• Bachelor’s degree in Computer Science, Data Science, Mathematics, Engineering, or related field, or equivalent work experience.
• Ability to obtain and maintain Public Trust clearance.
• Proven experience building and deploying AI/ML models in production environments using Python and cloud-native tools.
• Hands-on experience with Azure Machine Learning, Azure AI Services, or similar cloud AI platforms.
• Experience developing and deploying AI/ML solutions using Databricks or comparable cloud-native data platforms.
• Experience implementing MLOps practices, including model training pipelines, model registry, and automated deployment workflows.
• Demonstrated experience with natural language processing, generative AI, or predictive modeling techniques.
• Experience collaborating with cross-functional engineering, data, and business teams to translate AI/ML …