Machine Learning Engineer

Department: Data Analysis and Technology Services

Employment Type: Full Time

Location: Herndon, VA

Compensation: $130,000 - $260,000 / year

Description

Are you ready to apply machine learning techniques and algorithms to models and systems? Ardent Principles is searching for a full-time Machine Learning Engineer to join our dynamic team onsite in Herndon, VA.

If you're passionate about designing and implementing machine learning solutions to solve complex problems; thrive on collaboration; and are ready to make a significant impact, this is the opportunity you've been waiting for. With a competitive salary range of $130,000 to $260,000 annually, dependent on level of skills and experience, and industry-leading benefits, Ardent Principles offers more than just a job - we offer a career path filled with growth and opportunities. Join us and let's shape the future together!

Who We Are:

Passionate Integrity, Driven by Excellence
"Ardent Principles" signifies our unwavering commitment to excellence, driven by a profound passion and a strict adherence to ethical values. We believe that happy employees make for happy clients. Our mission is to act as a bridge between satisfied clients and fulfilled employees, ensuring that your job and well-being are our top priorities because your satisfaction leads to the success of our clients.

Key Responsibilities

In this challenging yet rewarding role as a Machine Learning Engineer, you are an integral part of what brings our company's mission to life. You are primarily responsible for and/ or have demonstrated experience in, with or using:

• Cybersecurity, IT systems, and A&A processes.
• Facilitation across multi-contractor and staff teams, collaborating with project teams or multiple entities to 
  define project requirements and acceptance criteria.
• Monitoring project progress, identifying roadblocks, 
  and implementing mitigation strategies.
• Designing, building, and maintaining data pipelines for data ingestion, processing, and transformation.
• Collaborating with data scientists and analysts to ensure data quality and accessibility.
• Building and implementing data governance policies and
  procedures for data integrity and security.
• Design, implementation, and management of secure and scalable cloud infrastructure solutions.
• Implementation and management of infrastructure-as-code
  solutions and collaboration with development teams.
• Implementation of CI/CD pipelines and automation of 
  infrastructure provisioning, application deployment, and
  system monitoring.
• Envisioning and delivering solutions to business problems leveraging machine learning techniques, to include: model training and development; full lifecycle model management. 
• Technical architecture expertise, including systems 
  integration and technical leadership. 
• Proficiency in complex multi-network and security enclave
  environments.

Highly Desired Qualifications

Other skills and demonstrated experiences that are highly desired but not mandatory to perform the work, include working with; using; familiarity with:  

• Championing SecDevOps best practices for collaboration, efficiency, and quality. 
• Deploying and managing solutions utilizing retrieval 
  augmented generation techniques. 
• Big data processing and analysis tools such as Splunk, SOAR, NiFi, or Cribl. 
• Data visualization and data lake house technologies. 
• AWS environment networking, security, logging, administration, and provisioning. 
• Specific accreditation and security, including security controls, system hardening, and compliance requirements.
• Data transfer tools such as NiFi or Cribl. 
• Creating ad-hoc scripts in Python and JSON. 
• Troubleshooting network connections or scanning. 
• JIRA or another ticketing system for task tracking.
• Familiarity with DevOps lifecycle and ability to coordinate
  requirements with development teams.
• Agile and Scrum methodologies for software 
  development. 
• Technical proficiency in Linux system administration and DevOps tools.
• Software engineering, cloud computing, data management, and system integration 
• Machine learning frameworks such as TensorFlow, PyTorch,
  and deploying models in cloud environments (SageMaker)
  data engineering and feature engineering for big data
  processing and machine learning algorithms.
• Understanding cloud platforms (AWS, OCI, Azure, or GCP) and best practices for cloud-based services and security.

Bachelor’s Degree in Information Technology or related discipline or