Senior AI/ML Engineer - TS/SCI

Parsons Corporation

2d•Hybrid

About The Position

Parsons is looking for an amazingly talented Senior AI/ML Engineer to join our team! In this role you will get to prototype, develop, and deliver AI-enhanced products into impactful customer-centric solutions. This role is hybrid, with a requirement to be onsite in Maryland three days per week

Requirements

B.S. in Computer Science, Cybersecurity, Computer Engineering, Data Science, or related discipline. Additional years of experience may be considered in lieu of degree.
Minimum five (5) years of experience in AI/ML projects including data engineering, application software development, and/or system integration.
Active Top Secret clearance with Polygraph eligibility

Nice To Haves

Experience in Department of Defense (DoD) Cyber Operations domains.
Excellent communication and teamwork skills.
Programming experience with Java, Python, or C++.
Experience working in air-gapped and secure cloud environments such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and/or Azure.
Proficient working with statistical analysis at scale.
Experience developing, debugging, and tuning data processing.
Experience working with modern tools for big data processing and scalable machine learning (e.g., AWS, GCP, Kafka, Apache Spark, Hadoop, SQL, NoSQL).
Interest in learning and researching AI/ML and latest data science tradecraft and technology.
Familiarity with Agile and Scaled Agile (SAFe) project planning.

Responsibilities

Passion for matching data driven AI/ML systems with the problems they solve.
Architect and develop AI solutions into existing programs and products.
Refine solutions using available data in analytical models and test cases.
Experience with graph and relational databases and diverse data formats including text, JSON, and Pandas data frames.
Present results and ideas to key decision makers, customers, analysts, and other stakeholders.
Consult and refine features and objectives with stakeholders.
Derive from the system requirements an understanding of stakeholder needs, functions that may be logically inferred and implied as essential to system effectiveness.
Translate these requirements to low-level implementations to fulfill needs.
Configure, support, troubleshoot, and integrate data solutions into cloud and virtual environments/infrastructure including AWS.
Building required infrastructure and architecture for optimal extraction, transformation and loading of data from various data sources.
Curate and collect data from a variety of traditional and non-traditional sources.
Collaborate with team members to monitor and analyze model performance and data accuracy.
Perform documentation of all processes, models, and activities.