SCI w Poly

Peraton•Laurel, MD

About The Position

We are seeking a highly skilled AI/ML Engineer Technology Lead to design, development, and deployment of advanced Artificial Intelligence and Machine Learning solutions supporting mission-critical enterprise initiatives. This role serves as the technical lead for enterprise AI/ML efforts, responsible for building, growing, and guiding a team of AI/ML engineering and practitioners. This individual collaborates with customer technical leadership to define and realize their AI/ML roadmap and is expected to bring both deep technical expertise and the business acumen necessary to develop strategy, manage stakeholder relationships, and scale a high-performing team. The ideal candidate combines deep expertise in machine learning, software engineering, cloud-native technologies, and MLOps with strong leadership and customer engagement skills. This individual will help transform AI/ML prototypes into scalable, secure, production-ready enterprise solutions while mentoring technical teams and collaborating with customer leadership.

Requirements

17 years with a Bachelor’s degree in Computer Science, Engineering, Data Science, Advanced Mathematics, or related technical field, or 10 years with a Master's Degree in Computer Science, Engineering, Data Science, Advanced Mathematics, or related technical field, or 7 years with a Ph.D in Computer Science, Engineering, Data Science, Advanced Mathematics, or related technical field
Strong proficiency with Python and AI/ML frameworks such as TensorFlow, PyTorch, Scikit-learn, or Keras
Experience with cloud platforms including AWS, Azure, or Google Cloud Platform
Experience with MLOps, CI/CD pipelines, Docker, Kubernetes, and distributed systems
Strong understanding of data pipelines, model lifecycle management, and scalable AI architectures
Experience operating within Agile or DevSecOps environments
Excellent communication and stakeholder engagement skills
Active/current TS/SCI security clearance with a current polygraph is required.

Nice To Haves

Experience supporting Intelligence Community (IC), DoD, or federal government programs
Experience with Large Language Models (LLMs), Generative AI, RAG architectures, and vector databases
Familiarity with AI governance, explainable AI (XAI), and responsible AI practices
Experience with Spark, Kafka, Hadoop, or GPU-enabled distributed computing platforms

Responsibilities

Serve as the primary point of contact (POC) for all AI/ML-related technical matters with customers and stakeholders
Lead the design, development, testing, deployment, and productization of AI/ML technologies supporting mission-critical objectives
Develop and communicate AI/ML strategies and technical roadmaps aligned with customer and mission requirements
Advise leadership on emerging AI/ML technologies, research areas, and industry advancements
Represent AI/ML capabilities during customer engagements, briefings, demonstrations, and technical reviews
Provide technical leadership, mentorship, and guidance to AI/ML engineering teams
Support workforce planning, staffing strategy, hiring, and professional development initiatives
Design, develop, and optimize scalable machine learning and deep learning models for structured and unstructured data
Transform research prototypes into secure, scalable, production-ready enterprise solutions
Support NLP, predictive analytics, anomaly detection, computer vision, and generative AI initiatives
Evaluate and productize AI/ML models across commercial and government cloud architectures
Integrate AI/ML solutions across enterprise systems, data platforms, and operational environments
Ensure AI/ML solutions meet performance, scalability, reliability, and operational requirements
Develop production-grade AI/ML pipelines, CI/CD workflows, and MLOps capabilities
Provide system integration oversight across AI/ML pipelines and enterprise platforms
Collaborate with software engineers, cloud architects, cybersecurity teams, and customer stakeholders
Support deployment and optimization of AI/ML solutions across cloud-native and distributed environments
Evaluate emerging technologies and align technical solutions with operational and enterprise objectives