Machine Learning Engineer

Capgemini Government Solutions (CGS) LLC is seeking a highly motivated Machine Learning (ML) Engineer with an active security clearance to deliver high-quality code components to power the services, servers, distributed systems, and backend architecture. The successful candidate will have the opportunity to conceive and deliver innovative ML solutions to the U.S. Public Sector to help address critical national and global challenges.

This individual will join our Data and AI practice in the DC Metro Area. As a part of the rapidly expanding CGS Data and AI practice, the candidate will also help our clients develop, deploy, and modernize their data estates and pipelines. At Capgemini, we are committed to our staff’s professional development and offer a wide range of training and educational resources.

In addition to our internal learning sites, we partnered with Coursera and Degreed to offer our staff the latest courses from academic institutions around the world. We provide education expense reimbursements as well as sponsor seminars, conferences, and certifications. Our practice leaders work with every team member to chart appropriate career paths and goals to ensure that we all stay innovative and transformative, which maximizes our ability to scale up our solutions, keep up with the cutting edge, and bring the art of what’s possible to the Federal Government.

Job Responsibilities As a ML Engineer you will:
Deliver high-quality code components that will power the services, servers, distributed systems, and backend architecture for Microsoft products Partner with industry-leading Engineers, Artists, Producers and Designers Incorporate the latest AI, Machine Learning and Computer vision capabilities into the design of Microsoft products and services Drive ML-related solutions based on evaluation of requirements, resources, and alternatives Conduct exploratory data analysis to evaluate data pipelines and construct data stores (structured, semi-structured, and unstructured) as needed to feed frameworks/models Develop custom algorithms, frameworks, and models or leverage available tools, libraries, and applications to solve complex problems Deploy ML solutions and develop methodologies to scale up Present and articulate findings and present solutions to clients and team members Maintain knowledge of advances of ML in industry and academia As needed, collaborate with internal and external stakeholders to identify object detection, optical character recognition, automation, predictive modeling, pattern analysis, natural language processing, fraud detection, and other business cases for using ML

Required Qualifications U.S. Citizenship and an active secret level security clearance or higher is required Ability to be at client site full time in Washington, DC Bachelor’s degree or higher in machine learning, data science, statistics, computer science, economics, mathematics, information systems, or similar field preferred Minimum of two (2) years of professional experience with machine learning-delivery responsibilities such as:
Deliver high-quality code components to power services, servers, distributed systems, and backend architecture Incorporate the AI, machine learning, and Computer vision capabilities into solutions and services Experience in object detection and computer vision models such as YOLO, MM Detection, R-CNN, SSD, FPN, and Retina Net Experience programming in languages such as Python, R, Scala, SQL, JavaScript, C/C++, and Java Experience using libraries and frameworks such as Tensor Flow, PyTorch, Spark ML/MLlib, and Jupyter Excellent verbal and written communication skills Ability to multi-task and stay flexible in a dynamic work environment Nice to have skills/qualifications Active TS clearance Data Science, ML, AI, or Cloud certifications Experience working in an IT project team following SDLC and Dev Ops methodologies Experience working with big data distributed programming languages and ecosystems such as Hadoop, Map Reduce, Pig, or Kafka

Experience with designing and building cloud-based databases, data lakes, and data warehouses Experience using tools such as Azure’s Machine…