C2 SMART Lead AI Engineer

i4DM•Millersville, MD

15h

About The Position

Are you ready to lead the charge in shaping the future of AI innovation? We’re seeking Lead AI Engineer who thrives at the cutting edge of technology, with a passion for solving complex challenges and transforming ideas into reality. In this role, you’ll spearhead the development of groundbreaking AI solutions that power mission-critical operations for the U.S. Army. As a senior member on our team, you’ll design and deploy state-of-the-art models- ranging from deep learning to large language models (LLMs) to revolutionize processes, automate workflows, and unlock new levels of efficiency. Beyond the code, you’ll collaborate with brilliant engineers and subject matter experts to push boundaries and redefine what's possible. You'll play a pivotal role in integrating cutting-edge AI capabilities into operational systems, bridging the gap between innovation and execution. This is not just a job, it’s a chance to be a part of something bigger, where your creativity and technical expertise directly contribute to national security and digital transformation. If you’re energized by solving real-world problems, working on a diverse team, and making a tangible impact, this is the role for you.

Requirements

A minimum of 6 years of related experience in the area of Machine Learning/AI with a Bachelor’s degree; or equivalent combination of related education and work experience.
Expertise with entity extraction, intent detection, contextual understanding, and other advanced NLP techniques within machine learning frameworks.
Strong experience in deep learning model design and development, including classification and generative models.
Skilled in machine learning optimization, focusing on feature selection, metrics analysis, and hyperparameter tuning.
Hands-on experience with large language models (LLM), small language models (SLM), domain specific language models (SDLM), and Retrieval-Augmented Generation (RAG).
Proven ability to deploy AI models to cloud platforms (CoPilot, Azure ML, and/or Amazon Bedrock), including real-time performance monitoring.
Experience in building platform capabilities to automate ML/LLM model deployment and scaling, as well as standardizing data pipeline deployments for model consumption across various LOBs.
Strong ability to collaborate in a cross functional team, including product management, Engineering, DevSecOps, among other stakeholders; and lead AI use case delivery from concept to deployment.
Preferred candidate will have significant substantive experience with mission IT-focused AI solutions.
Preferred candidate will demonstrate experience and capability to advise the federal government on all aspects of the AI domain to implement and adopt innovative AI solutions.
Military-experienced candidates are encouraged to apply.
Active Secret clearance or ability to obtain one.

Nice To Haves

Mission/defense experience; familiarity with C2, COP integration, and tactical edge constraints.
Familiarity with Meibel (or strong aptitude to adopt rapidly) for orchestration and lifecycle management.
API architecture and design strategy.
API development (consumer and producer).
API performance tuning.
VMF (Variable Message Format) and JSON message formats.
Disconnected operations experience (e.g., ATAK to TAK server, TAK server to TDP connections).
Appian application architecture and Appian data fabric.
Data tagging (Appian/Kafka) and data aggregation.
Kafka architecture and Kafka development.

Responsibilities

Design, build and deploy advanced machine intelligence applications such as digital agents (chatbots) and pattern recognition systems for text, image, and speech recognition.
Develop and optimize Natural Language Processing (NLP) systems, with a focus on entity extraction and machine learning-driven language understanding.
Apply expertise in large language models (LLM), Domain Specific Language Models (DSLMs) and Retrieval-Augmented Generation (RAG) to create scalable, high-performance language models that drive business and product innovation.
Architect models for small servers and constrained hardware (e.g., ruggedized x86/ARM, NVIDIA Jetson, Intel NUC).
Define the edge vs. core processing strategy (on device inference, partial offload, opportunistic sync) for disconnected/low bandwidth conditions.
Deploy machine learning models into larger systems, ensuring seamless integration and monitoring real-time performance. Implement feedback loops to continuously optimize models based on production data.
Develop Generative AI & Traditional AI platform capabilities on enterprise on-prem and cloud platforms.
Build automation capabilities for ML and LLM model deployment to on-prem and cloud platforms (e.g., GCP-Vertex AI, Azure ML).
Standardize model consumption and data pipeline deployment, enabling multiple Lines of Business (LOB) to utilize the deployed models efficiently.
Work closely with UX/UI designers and front-end developers to create interfaces that enhance the user experience of AI-powered tools such as chatbots and dashboards.
Collaborate with data engineers to optimize the scoring pipeline for AI models, ensuring high-performance scoring and inferencing capabilities for ML models.
Work with product owners, DevSecOps teams, data engineers, and support teams to define and drive end-to-end model scoring pipelines, ensuring seamless deployment and scalability.
Design, build, and deploy artificial intelligence solutions that empower humans to make more informed decisions.
Participate in day-to-day standups to contribute to platform capability development and ensure alignment across teams.
Provide Subject Matter Expertise (SME) guidance to engineers on software engineering principles, model training and deployments, and platform capabilities.
Lead AI use case delivery, collaborating with business subject matter experts, data engineers, security engineers and LOB technology teams using standardized platform processes and capabilities.