Machine Learning Engineer

Capgemini•Washington, DC

1d•$120,000 - $180,000•Onsite

About The Position

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.

Requirements

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, MMDetection, R-CNN, SSD, FPN, and RetinaNet
Experience programming in languages such as Python, R, Scala, SQL, JavaScript, C/C++, and Java
Experience using libraries and frameworks such as TensorFlow, 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 Haves

Active TS clearance
Data Science, ML, AI, or Cloud certifications
Experience working in an IT project team following SDLC and DevOps methodologies
Experience working with big data distributed programming languages and ecosystems such as Hadoop, MapReduce, Pig, or Kafka
Experience with designing and building cloud-based databases, data lakes, and data warehouses
Experience using tools such as Azure’s Machine Learning and Cognitive Services; AWS’s SageMaker, Polly and Rekognition; DataRobot; or H2O.ai

Responsibilities

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