Lead AI Engineer (Computer Vision & Fusion)

Oslitandi Tech LLC Lead AI Engineer (Computer Vision & Fusion) Washington, DC·Full time Company website

This is a senior technical leadership role focused on creating the core AI capability for the division. The engineer will be responsible for architecting, training, and optimizing deep learning models (CNNs, Transformers) for object detection and classification. A primary function is developing advanced sensor fusion algorithms (Kalman Filters) to integrate and correlate multi-sensor data (radar, optical) into a cohesive, low-latency intelligence product for critical C2 operations, deployable on edge hardware.

About Oslitandi Tech LLC

Our company works with clients to achieve their tactical and strategic goals by unifying sustainable technology solutions which reduce costs, decrease cycle times, and seamlessly manage processes throughout the enterprise.

Oslitandi Tech specialty lies in integrating sustainable IT services & solutions, Management & IT Consulting, & Network/Cyber Security.

Description

Primary Responsibilities (Operational Duties)

• Deep Learning Architecture: Architect, design, and train advanced deep neural networks (e.g., CNNs, Transformers, R-CNN variants) specifically optimized for object detection, classification, and tracking across multi-modal sensor inputs (e.g., High-resolution Optical, SAR, Radar).
• Sensor Fusion Expertise: Lead the development and implementation of advanced sensor fusion algorithms, including extended and unscented Kalman Filtering and particle filters, to reliably correlate and maintain track continuity from disparate, asynchronous data sources (e.g., Radar + Electro-Optical/IR + ADS-B).
• Optimization for Edge Compute: Conduct model optimization, pruning, and quantization techniques to achieve ultra-low-latency inference (sub-10ms) required for deployment on specialized, resource-constrained edge compute hardware .
• Synthetic Data Generation: Work directly with forensic data scientists to conceptualize and develop tooling to generate high-fidelity synthetic training scenarios to effectively address data sparsity for rare, critical threat events.
• Full ML Lifecycle Management: Oversee the model experimentation, versioning, quality assurance (QA), and transition process into the MLOps pipeline maintained by the DevSecOps team.
• Technical Leadership: Manage multiple, concurrent AI research and development assignments, providing technical guidance, code review, and mentorship to junior engineers within the AI/ML Squad.

Basic Qualifications (Experience & Technical Stack)

• A minimum of 7+ years of progressive experience in Machine Learning, AI research, or applied data science.
• At least 3+ years dedicated experience in the fields of Computer Vision, Object Detection/Tracking, or Multi-Sensor Fusion.
• Expert-level proficiency in Python and deep learning frameworks: PyTorch (preferred) or TensorFlow .
• Demonstrated practical experience utilizing specific detection methodologies (e.g., YOLO family, Faster R-CNN) and/or classical tracking methods (e.g., Kalman Filtering ).
• Proficiency in containerization technologies, specifically Docker , for reproducible development and deployment environments.
• Experience with forensic data analysis, data labeling processes, and generating synthetic data for specialized training use cases.
• The candidate shall have a Master's or PhD in Computer Science, AI, Mathematics, or a related quantitative field.
• Must be eligible for a U.S. Government TS/SCI Clearance .