Associate AI/ML Engineer

eSimplicity•Columbia, MD

just now•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 AI/ML Engineer to join our data analytics team. In this role, you will support the design, deployment, and operation of production-grade A.I. systems - using analytics to shorten time to action. This role supports end-to-end model lifecycle engineering on Azure, advance MLOps best practices, and building A.I. agents 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

Bachelor’s degree in computer science or related field
Experience: 3+ years hands-on developing and deploying AI/ML models in production environments
Programming: Proficient in Python (including packaging, testing, performance optimization)
ML Expertise: Understanding of algorithms, model selection, training/validation/optimization, and evaluation at scale
Data Skills: Proficient in data preprocessing, feature engineering, and data visualization for decision support
Deep Learning & MLOps: Proficient with PyTorch/TensorFlow, and modern MLOps (deployment, monitoring, scaling, CI/CD, experiment tracking, model registry)
Cloud: Experience with Azure for AI/ML workloads (e.g., Azure ML, Azure Synapse, Azure Data Lake)
AI Agents: 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
Certifications in Azure AI/ML and/or?MLOps are advantageous

Responsibilities

Implement and productionize ML solutions (supervised/unsupervised, NLP, deep learning) with robust data preprocessing, feature engineering, and evaluation pipelines
Support 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.
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
Engage closely with project managers, technical leads, client representatives, and cross-functional teams to provide timely updates, resolve issues, and ensure alignment with technical goals
Translate technical specifications into code and design documents