Senior AI Value Stream Engineer

Job Description

Job Description

• Position Overview: Our client is seeking an experienced Senior AI Value Stream Engineer to act as the primary orchestrator for a public sector artificial intelligence transformation initiative. The position ensures AI initiatives move through the developmental value stream with minimal friction to translate specialized investments into measurable operational efficiency gains. This is a full-time, direct-hire (permanent) position operating on an on-site basis with some hybrid flexibility in Linthicum Heights, MD.
• Core Responsibilities:
• Manage day-to-day project activities and oversee comprehensive IT project operations as the primary single point of contact.
• Identify operational risks, establish issues tracking metrics, and recommend targeted technical risk mitigation strategies.
• Facilitate strategic alignment discussions and cross-functional meetings between agency stakeholders and external contractors.
• Monitor project timelines to ensure that performance matches defined scope, meets technical requirements, and delivers on time and within budget.
• Define critical deployment paths, core tasks, operational dates, technical testing, and stakeholder acceptance criteria.
• Enforce strict adherence to state Software Development Life Cycle (SDLC) design standards.
• Deliver innovative process solutions to maximize budget efficiency and reduce execution costs while maintaining high-performance levels.
• Document and deliver formal project management artifacts, lifecycle blueprints, and real-time status updates.
• Optimize AI workflows by identifying and removing operational bottlenecks related to data acquisition, model training, and ethical review cycles.
• Integrate Scaled Agile Framework (SAFe) environments with active Machine Learning pipelines to ensure experimentation phases transition smoothly into production delivery phases.
• Define, map, and track AI-specific key performance indicators (KPIs) including inference cost vs. value, model accuracy over time, and task automation return on investment (ROI).
• Act as an AI Governance Liaison alongside the Security Officer to embed compliance architectures, bias testing, and security guardrails into the Agile Release Train (ART).
• Coordinate vendor and open-source model evaluation strategies to ensure technical tools match long-term value stream architectures.
• Coach scaling Agile engineering teams through complex technical transitions.

Required Qualifications & Skills:

• A Bachelor's degree from an accredited college or university in an IT, business, or related scientific discipline.
• A minimum of 4 years of hands-on experience managing complex IT software development projects.
• At least 3 years of experience operating with a strong foundational knowledge of SAFe working methodologies.
• A minimum of 2 years of active experience in SAFe environments, with a proven ability to adapt Lean-Agile principles to data-heavy or algorithmic projects.
• At least 2 years of dedicated experience managing complex IT projects specifically involving Natural Language Processing (NLP), Predictive Analytics, or Generative AI.
• Proven experience serving in a leadership capacity for at least 2 successful projects containing an organizational change management component.
• Demonstrated history leading at least 2 successful projects involving change management frameworks that trained non-technical staff to adopt AI tools.
• Native or bilingual English language proficiency.

Preferred Qualifications & Skills:

• Demonstrated experience overseeing the transition of AI/ML projects from Proof of Concept (PoC) to full-scale production.
• Experience managing variable cloud compute budgets, tracking GPU usage, and measuring API consumption costs.
• Comprehensive understanding of the "Data-to-Model-to-App " pipeline with an ability to bridge technical communications between data scientists and infrastructure teams.
• Specialized experience identifying and mitigating distinct AI risks, including Data Drift, Model Hallucinations, and Algorithmic Bias.
• Knowledge of foundational DataOps and MLOps operational principles.