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Senior Principal AI/ML Technical Architect & Portfolio Owner

LCG, Inc.
$190,000.00 - $240,000.00 / yr
retirement plan
United States, Maryland, Rockville
6000 Executive Blvd Ste 410 (Show on map)
Oct 07, 2025

Location: Rockville, MD

Work Type: Hybrid Work (Minimum 2 days Onsite - Could extend to more days because of Client meetings and Proposals)

Required Clearance: Ability to Obtain Public Trust

Job Title: Senior Principal AI/ML Technical Architect & Portfolio Owner

Job Overview: LCG Inc. is seeking a Senior Principal AI/ML Technical Architect & Portfolio Owner to lead the strategy, architecture, and delivery of AI/ML capabilities across a portfolio of federal IT programs. You will shape an enterprise-grade, secure, and explainable AI ecosystem-spanning data pipelines, MLOps, model governance, and mission applications-while partnering in a joint leadership model with the Director, Portfolio Management & IT Programs. This is a hands-on role for an applied AI leader who can design end-to-end solutions, produce rapid POCs, and mentor teams-bridging business outcomes with modern AI/ML practices.

Key Responsibilities

Portfolio-Focused AI/ML Leadership



  • Serve as the technical authority for all AI/ML solutions within your assigned portfolio, spanning data analytics, automation, and intelligent systems.
  • Lead the definition of AI/ML roadmaps, technical standards, and platform choices aligned with mission priorities.
  • Architect secure and explainable AI systems, ensuring compliance with FISMA, FedRAMP, and emerging AI governance regulations.
  • Guide modernization efforts that integrate data platforms, model pipelines, and analytics environments into cloud-native architectures.


Innovation and Strategic Advisory



  • Design, prototype, and validate AI/ML architectures that integrate with enterprise systems, including data lakes, microservices, and API-driven applications.
  • Lead the creation of proof-of-concepts (POCs) and pilots to demonstrate real-world application of AI/ML to business challenges.
  • Collaborate with data engineers, scientists, and developers to deploy models through CI/CD-enabled MLOps pipelines (MLflow, Kubeflow, Jenkins, etc.).
  • Ensure solutions are observable, measurable, and maintainable, including automated retraining and explainability tools.
  • Troubleshoot production AI systems, ensuring uptime, performance, and responsible operation.


Cross-Enterprise Collaboration and Knowledge Sharing



  • Partner with other portfolio architects to build enterprise-wide AI frameworks and reusable design patterns.
  • Conduct cross-portfolio architecture review boards to assess solution scalability, security, and ethical AI adherence.
  • Contribute to LCG's AI initiatives, curating internal playbooks, templates, and data model catalogs.
  • Share lessons learned and promote standardization across LCG's AI/ML ecosystem.


Capture, Proposal, and Client Engagement



  • Lead AI/ML solution development in support of RFI/RFP capture, including architecture diagrams, white papers, and technical volumes.
  • Participate in client orals, representing LCG's AI/ML strategy as a thought leader.
  • Develop cost and resource estimates for AI/ML components in new bids or recompetes.
  • During project transitions, ensure knowledge transfer, continuity, and system handover of AI/ML assets.


Mentorship, Enablement, and Workplace Development



  • Mentor teams of engineers, data scientists, and analysts in best practices for ML engineering, MLOps, and ethical AI.
  • Create learning roadmaps and training sessions for internal upskilling in data science and AI tooling.
  • Foster a culture of experimentation, continuous learning, and responsible innovation.


Governance, Compliance, and Documentation



  • Implement governance practices for model explainability, bias detection, and performance auditing.
  • Ensure documentation covers data flows, architectural decisions, and compliance evidence (e.g., AI risk assessments).
  • Partner with cybersecurity and compliance teams to ensure all AI systems align with federal data protection and transparency mandates.


Qualifications



  • Bachelor's degree in Computer Science, Data Science, Engineering, or related field.
  • 12+ years in IT, with at least 7+ years in AI/ML architecture, data science, or applied ML leadership roles.
  • Proven track record designing, deploying, and scaling AI/ML systems in regulated or federal environments.
  • Deep understanding of:

    • AI/ML frameworks: TensorFlow, PyTorch, Scikit-learn, Hugging Face, LangChain, MLflow, Kubeflow, Airflow, DVC. Azure ML, Microsoft Agentic Framework, CoPilot, Enterprise Open AI for public sector etc. Experience with emerging AI orchestration frameworks such as Semantic Kernel, Agno, AutoGen, LangChain, and LlamaIndex for building intelligent multi-agent and retrieval-augmented systems.
    • Generative AI Ecosystems: Hands-on experience with Azure OpenAI Service, AWS Bedrock, or Google Cloud Vertex AI, including prompt engineering, fine-tuning, RAG (Retrieval-Augmented Generation) implementations, vector database integration, and deployment of custom LLM applications.
    • Additional Expertise: Knowledge of responsible AI principles, and leveraging GenAI APIs to automate workflows, enhance productivity, and enable conversational or document intelligence solutions.
    • Cloud ML services: Azure ML, AWS SageMaker, GCP Vertex AI
    • Data & MLOps tools: Data Analytics Platform like Microsoft Fabric, Amazon RedShift, Google BigQuery for AI/ML data transformation implementation.
    • Experience designing and implementing GenAI based applications, using Agentic design, MCP Servers etc.
    • Good knowledge and AI implementation exposure of using Vector Databases like PostSQL, Aurora, Cosmos DB, Dynamo DB etc.


  • Strong foundation in data governance, security, and compliance.
  • Excellent communication and stakeholder engagement skills.


Preferred:



  • Master's or PhD in AI, Machine Learning, Data Science, or related discipline.
  • Experience in federal contracting (HHS, NIH, DOD, etc.).
  • Knowledge of Generative AI, RAG architectures, and agentic frameworks.
  • Certifications (e.g., Azure AI Engineer, AWS ML Specialty, TOGAF).
  • Experience building reusable AI/ML accelerators, reference architectures, and proposal solutions.


Compensation and Benefits

The projected compensation range for this position is $190,000 to $240,000 per year benchmarked in the Washington, D.C. metropolitan area. Salary at LCG is determined by various factors, including but not limited to role, location, the combination of education/training, knowledge, skills, competencies, certifications, and work experience.

LCG offers a competitive, comprehensive benefits package which includes health insurance options (medical, dental, vision), life and disability insurance, retirement plan contributions, as well as paid leave, federal holidays, professional development, and lifestyle benefits.

Devoted to Fair and Inclusive Practices

All qualified applicants will receive consideration for employment without regard to sex, race, ethnicity, age, national origin, citizenship, religion, physical or mental disability, medical condition, genetic information, pregnancy, family structure, marital status, ancestry, domestic partner status, sexual orientation, gender identity or expression, veteran or military status, or any other basis prohibited by law.

If you are interested in applying for employment with LCG and need special assistance or an accommodation to apply for a posted position, contact our Human Resources department by email at hr@lcginc.com.

Securing Your Data

Beware of fraudulent job offers using LCG's name. LCG will never request payment-related details or advancement of money during the application process. Legitimate communication will only come from lcginc.com or system@hirebridgemail.com emails, not free commercial services like Gmail or WhatsApp. If you receive suspicious emails asking for payment or personal information, contact us immediately at hr@lcginc.com.

If you believe you are the victim of a scam, contact your local law enforcement and report the incident to the U.S. Federal Trade Commission.

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