Senior AI/ML Engineer

eSimplicity•Columbia, MD

1d•Hybrid

About The Position

eSimplicity is a modern digital services company that partners with government agencies to improve the lives and protect the well-being of all Americans, from veterans and service members to children, families, and seniors. Our engineers, designers, and strategists cut through complexity to create intuitive products and services that equip federal agencies with solutions to courageously transform today for a better tomorrow. eSimplicity is seeking a skilled and motivated Senior AI/ML Engineer to join our data analytics team. In this role, you will be responsible for leading end-to-end model lifecycle engineering on Azure, advance MLOps best practices, and build AI agents (Copilot Studio + Python frameworks) that translate signals into timely, actionable decisions. Successful candidates will be eligible to hold a U.S. Federal Public Trust security clearance. This position is contingent upon contract award.

Requirements

PhD or Master’s degree in Computer Science, Data Science, Mathematics, Statistics, Engineering, OR related field; equivalent education as determined by the organization may be considered where permitted by law.
8+ years hands-on developing and deploying AI/ML models in production environments.
Python (including packaging, testing, performance optimization).
Deep understanding of algorithms, model selection, training/validation/optimization, and evaluation at scale.
Data preprocessing, feature engineering, and data visualization for decision support.
Proficiency in PyTorch/TensorFlow, and modern MLOps (deployment, monitoring, scaling, CI/CD, experiment tracking, model registry).
Proven experience with Azure for AI/ML workloads (e.g., Azure ML, Azure Synapse, Azure Data Lake).
Experience developing AI agents in Copilot Studio and via Python frameworks (tooling, orchestration, retrieval, connectors).
Strong attention to detail with a commitment to delivering high-quality and accurate work.
Excellent communication skills, both written and verbal, with the ability to collaborate effectively across teams.
Proven ability to manage time and prioritize tasks in a fast-paced environment.
Demonstrated problem-solving skills with a proactive and solution-oriented mindset.

Nice To Haves

Experience with streaming/event-driven architectures (Event Hubs), feature stores, and vector databases (for retrieval augmented generation).
Hands-on with responsible AI (fairness, explainability, privacy), model governance (model cards, audits), and security in cloud ML.
Familiarity with domain-specific risk analytics and public sector/regulated environments.
Soft leadership skills – including mentoring of other technical personnel on the program.
Certifications in Azure AI/ML and/or MLOps advantageous.

Responsibilities

Architect, implement, and productionize ML solutions (supervised/unsupervised, NLP, deep learning) with robust data preprocessing, feature engineering, and evaluation pipelines.
Lead model selection, training, validation, optimization, and calibration, ensuring reliability, fairness, and performance at scale.
Establish MLOps workflows (CI/CD for ML, experiment tracking, model registry, reproducible builds and deployments).
Implement model monitoring (drift, data/feature quality, bias, and business KPIs), alerting, and automated rollback to keep systems safe and responsive.
Design high-quality data pipelines (ingest, transform, validate) across structured and unstructured sources; enforce data contracts and lineage.
Partner with analytics teams to make datasets discoverable, documented, and performant for iterative model development.
Build AI agents that operationalize safety analytics (Copilot Studio, Python agents, retrieval pipelines) to accelerate triage and decision flow.
Integrate agents with APIs, event streams, dashboards, and case management systems to reduce cycle time from signal to action.
Champion secure-by-design practices, reproducibility, and auditability (model cards, data sheets, deployment records).
Contribute to coding standards, code reviews, and knowledge sharing; mentor engineers and data scientists.
Work in Agile teams; drive iterative delivery, joint problem-solving, and continuous improvement.
Translate mission goals into technical roadmaps and measurable outcomes tied to Sentinel time-to-intervention targets.
Provide technical vision and direction to complex model-related initiatives. Offer guidance and oversight to junior personnel and contribute as a hands-on expert in the field.
Engage closely with project managers, client representatives, and cross-functional teams to provide timely updates, resolve issues, and ensure alignment with business goals.
Translate technical specifications into code and design documents