Artificial Intelligence / Machine Learning Developer

Unissant•Ashburn, VA

1d•Onsite

About The Position

Unissant, Inc. is seeking an Artificial Intelligence/Machine Learning Developer to join their team and support a federal customer. This role is client-facing and requires on-site work at the DHS customer location. The position involves developing, training, evaluating, fine-tuning, and deploying advanced AI/ML, computer vision, deep-learning, and GenAI models. The developer will design and implement innovative AI solutions using techniques such as computer vision, image analysis, natural language processing, and large language models. Responsibilities include preparing datasets, optimizing model performance, developing AI applications, collaborating with cross-functional teams, staying updated on AI advancements, conducting research, and ensuring compliance with data privacy and security requirements. The role also involves briefing stakeholders on technology solutions.

Requirements

Three (3) or more years of experience in the Information Technology field focusing on AI/ML engineering projects, computer vision, image analysis, biometric technologies, MLOps, DevSecOps, and technical architecture.
Demonstrated hands-on experience developing, training, fine-tuning, evaluating, or deploying computer-vision, image-analysis, deep-learning, or biometric AI/ML models.
Experience working with image-analysis use cases such as image classification, object detection, image segmentation, image matching, face detection, facial recognition, facial verification, image quality assessment, liveness detection, identity verification, fingerprint analysis, iris recognition, or related technologies.
Familiarity with biometric systems, biometric matching, identity resolution, facial recognition, fingerprint, iris, liveness detection, or image-quality analysis is required.
Proficiency in developing, deploying, and fine-tuning generative-AI models, including large language models (LLMs).
Strong proficiency in programming languages such as Python, R, Java, and C/C++.
Experience with machine-learning, deep-learning, computer-vision, and generative-AI frameworks.
Proficiency in ML modeling frameworks and libraries such as scikit-learn, TensorFlow, Keras, PyTorch, OpenCV, YOLO, Detectron, Hugging Face, or equivalent platforms.
Experience with natural-language-processing techniques, including text classification and language generation.
Experience developing and evaluating image-analysis models using image transformations, feature extraction, embeddings, image-data augmentation, annotation/labeling, and model-performance metrics.
Solid understanding of any cloud platform, such as AWS, Azure, or GCP, and deployment strategies for AI/ML, image-analysis, and computer-vision workloads.
Solid understanding of MLOps and DevSecOps practices for deploying AI/ML, computer-vision, and image-analysis models and applications.
Proficiency in front-end development technologies such as React, Angular, Vue.js, HTML, CSS, and JavaScript.
Knowledge of database systems, such as SQL, NoSQL, vector databases, graph databases, and data-warehousing concepts.
An understanding and competency surrounding data storage, access, ingestion, and loading.
Databases: PostgreSQL, NoSQL, vector databases, graph databases, or comparable technologies.
ETL/ELT concepts.
Data warehouse concepts.
SQL.
Competency in data exploration, analytics, data quality, image-data processing, and feature engineering.
Python experience with Pandas, NumPy, Polars, PySpark, OpenCV, Pillow, scikit-image, or comparable data and image-processing tools.
Experience with Plotly, Matplotlib, or equivalent data-visualization tools.
Knowledge of data encoding, normalization, regularization, image preprocessing, data augmentation, and related data-preparation methods.
Understanding of deep-learning concepts and architectures such as CNNs, RNNs, LSTMs, GANs, transformers, vision transformers, and multimodal models, with the ability to apply them to real-world image, biometric, and mission datasets.
Proficiency in NLP tools such as SpaCy, Thinc, Gensim, or comparable frameworks.
Knowledge of GenAI tools, including Hugging Face models, OpenAI models, Grok, or comparable platforms.
General competency in various ML disciplines, including image classification, object detection, segmentation, facial recognition, biometric matching, forecasting, transformers, generative AI, anomaly detection, deep learning, and pattern recognition.
Enthusiastic, proactive, positive attitude with great listening skills, high integrity, and the ability to work effectively in a team environment.
Adaptability to changing priorities and a willingness to learn and grow are essential.
Excellent organizational skills and the ability to effectively manage concurrent projects.
Comprehensive problem-solving skills with exceptional attention to detail.
Ability to learn, evolve, think creatively, and work proactively.
Ability to work under pressure at times and to be extremely flexible with changing priorities.
Ability to work independently and in a team setting, take ownership of and complete relatively complex tasks, effectively using available resources, with minimal guidance.
Must have an active DHS clearance.
United States Citizens.

Nice To Haves

Any related systems engineering, computer vision, biometrics, data science, machine learning, cloud, or technical certifications are desired.
AWS/Azure/GCP AI/ML certifications are preferred but not required.

Responsibilities

Drive a big data approach to execute government requirements to manage, prepare, analyze, and enrich image, biometric, and related mission data to gather new insights.
Work as part of a team to provide consultative, architectural, program, and engineering support for a federal customer.
Develop, train, evaluate, fine-tune, and deploy advanced AI/ML, computer vision, deep-learning, and GenAI models.
Design and implement innovative AI solutions to address complex business challenges using techniques such as computer vision, image analysis, image classification, object detection, image segmentation, image matching, facial recognition, facial verification, biometric analysis, natural language processing, and large language models.
Prepare image, biometric, and related datasets for model development, including data extraction, cleaning, normalization, annotation, labeling, augmentation, feature engineering, data balancing, and training/validation/test-set construction.
Develop, train, evaluate, and improve image-analysis and computer-vision models used for biometric, identity-resolution, image-quality, matching, classification, detection, segmentation, or related mission capabilities.
Optimize model performance, ensuring accuracy, efficiency, scalability, bias awareness, and operational reliability.
Evaluate model performance using appropriate measures, including precision, recall, F1 score, confusion matrices, ROC-AUC, mean average precision, false-match rates, false-non-match rates, false acceptance rates, false rejection rates, latency, throughput, and related performance metrics.
Develop and maintain user-friendly AI applications and interfaces, including image-analysis tools, analytic dashboards, chatbots, virtual assistants, and generative content tools.
Collaborate with cross-functional teams to integrate AI solutions into existing systems and workflows.
Stay up to date with the latest advancements in AI/ML and emerging technologies, such as computer vision, biometrics, generative AI, reinforcement learning, and secure AI deployment.
Conduct research and experiments to explore new AI techniques and applications, including image-model training, computer-vision model evaluation, prompt engineering, advanced RAG, image retrieval, multimodal models, and fine-tuning LLMs.
Ensure compliance with data privacy, biometric-data handling, security, and governance requirements, especially when working with sensitive data, images, biometric information, and generative-AI outputs.
Brief the benefits and constraints of technology solutions to technology partners, stakeholders, team members, and senior levels of management.