AI​/ML Engineering Lead

At 4A Consulting, we turn complexity into opportunity. Founded in 2014 and headquartered in Baltimore, MD, we are a women-owned, boutique consulting firm specializing in delivering innovative, data-driven solutions to both the Federal Government and Fortune 500 clients. Our team blends deep industry knowledge with advanced technologies to design tailored strategies that drive measurable and sustainable outcomes. We pride ourselves on an agile, collaborative approach that helps organizations navigate challenges and seize emerging opportunities in a rapidly evolving digital landscape.

At 4A, we don’t just deliver projects, we build trusted partnerships that empower our clients to lead with confidence in the digital age.

The AI/ML Engineering Lead will lead the architecture, development, implementation, and governance of a shared enterprise Artificial Intelligence and Machine Learning (AI/ML) platform serving multiple state agencies. The role requires strong expertise in ML engineering, MLOps, cloud infrastructure, data engineering, and Generative AI to enable secure, scalable, reliable, and responsible AI/ML solutions. The Lead will establish technical standards, oversee production deployments, and collaborate with engineering teams, agency leadership, and policy stakeholders to deliver enterprise AI capabilities.

• Platform Architecture:
  Define the technical vision and roadmap; design scalable, secure, and cost-effective AI/ML infrastructure; and evaluate cloud services, frameworks, and tools, including AWS Sage Maker and equivalent Azure or GCP services.
• MLOps and Platform Engineering:
  Build end-to-end ML pipelines for data ingestion, feature engineering, model training, validation, versioning, deployment, and monitoring. Implement CI/CD, automated testing, canary deployments, rollback procedures, model registries, experiment tracking, and production support standards.
• Generative AI and LLMs:
  Design and deploy LLM and Retrieval-Augmented Generation (RAG) solutions, prompt management, model evaluation, and fine-tuning workflows. Implement safeguards against hallucinations, prompt injection, data exposure, and unfair or inappropriate outputs.
• Data Engineering and Governance:
  Oversee shared data pipelines, feature stores, data quality, lineage, and access controls. Ensure compliance with applicable state and federal data privacy and security requirements.
• Security and Responsible AI:
  Integrate identity and access management, encryption, audit logging, model governance, explainability, fairness, and risk assessment into platform operations.
• Technical Leadership and Operations:
  Lead and mentor ML, data, and platform engineers; establish engineering standards; conduct architecture and code reviews; define service-level objectives and agreements (SLOs/SLAs); manage production incidents; and optimize reliability, performance, scalability, and cost.
• Automation and Continuous Improvement:
  Implement infrastructure as code, Git Ops, observability, and standardized deployment practices. Partner with agency teams to onboard users and accelerate delivery of production-ready AI/ML solutions.

• Bachelor's degree in Computer Science, Data Science, Engineering, Information Technology, or a related field from an accredited college or university. A master's degree is preferred.
• At least sixteen (16) years of software or data engineering experience, including at least three (3) years focused on production ML systems, MLOps, or ML platform engineering.
• At least twelve (12) years of relevant general experience and ten (10) years of specialized IT experience directly related to the required area of expertise, subject to applicable contract labor-category requirements.
• Demonstrated experience building or operating shared enterprise AI/ML platforms supporting multiple teams, departments, or business units.
• Strong proficiency in Python and familiarity with ML frameworks including PyTorch, Tensor Flow, scikit-learn, and Hugging Face.
• Hands‑on experience with MLOps and workflow tools such as MLflow, Kubeflow, and Apache Airflow, or equivalent technologies.
• Experience with cloud AI/ML services on AWS, Azure, or GCP, including AWS Sage Maker or equivalent managed ML services.
• Experience with Docker, Kubernetes, CI/CD pipelines, model registries, experiment tracking, automated testing, model deployment, and monitoring.
• Working knowledge of LLMs, prompt engineering, RAG architecture, Generative AI…