AI Machine Learning Skill 3-FFPP-8953

REQUIRED:

TO BE CONSIDERED FOR THIS POSITION YOU MUST HAVE AN ACTIVE TS/SCI W/ FULL SCOPE POLYGRAPH SECURITY CLEARANCE (U.S. CITIZENSHIP REQUIRED)

The Artificial Intelligence/Machine Learning (AI/ML) Engineer designs, creates, tests, and productizes AI/ML algorithms to solve business challenges. The AI/ML models they create should be capable of learning and making predictions as defined by the business logic developed to meet customer requirements. The AI/ML Engineer should be proficient in all aspects of model architecture, data pipeline interaction, and metrics application, interpretation, and presentation. The AI/ML Engineer needs familiarity with foundational concepts of application development, infrastructure management, data engineering, and data governance. Through an understanding of training, retraining, deploying, scheduling, monitoring, and improving models through iterative user and system feedback, the AI/ML Engineer designs and creates scalable solutions for optimal performance. The AI/ML Engineer may be responsible for leading geographically diverse teams and will often serve as a primary POC for AI-related matters, so must have exceptional analytical, problem-solving and communication skills. Expert knowledge of multiple programming languages, e.g. Python, Java, C, R, a plus.

10 years experience deploying machine learning algorithms is required. A Master's or Ph.D. degree in advanced math, artificial intelligence, data science, computer science or deep learning from an accredited college or university is required. 7 additional years machine learning experience with a relevant Bachelor's degree may be substituted for a Master's degree. Demonstrated abilities in software engineering and AI/ML model test and evaluation.

Select appropriate data sets

Perform statistical analysis

Run machine learning algorithms

Use results to improve models

Train and retrain systems when needed

Experience in working with various ML libraries and packages

Run standard test and evaluation protocols

Provide system integration oversight

Oversee Test and evaluation of AI and ML algorithms through an iterative design process to meet verification and validation requirements

Research and implement a broad range of AI and ML algorithms and tools

Design or Select appropriate data and knowledge representation methods

Recognize software architecture, data modelling, and data structures

Transform and convert data science prototypes into scalable solutions

Verify data and model output quality

Identify differences in data distribution that affect model performance

Develop criteria for test and evaluation to include explainability and resiliency

Deep understanding of AI logic, semantics, ontologies, and knowledge representation

Demonstrated ability to design, evaluate, and productize AI/ML models on a range of commercial cloud-based architectures

Design ML algorithms according to customer requirements

Research, experiment with, and implement suitable ML algorithms and tools

Broaden current AIML frameworks and machine learning libraries.

Compensation: We are committed to providing fair and competitive compensation. The salary range for this position is $78,000 to $250,000 per year. This range reflects the compensation offered across the locations where we hire. The exact salary will be determined based on the candidate's work location, specific role, skill set, and level of expertise.

Benefits: We offer a comprehensive benefits package, including:

• Health Coverage: Medical, dental, and vision insurance

• Additional Insurance: Basic Life/AD&D, Voluntary Life/AD&D, Short and Long-Term Disability, Accident, Critical Illness, Hospitalization Indemnity, and Pet Insurance

• Retirement Plan: 401(k) plan with company match

• Paid Time Off: Generous PTO, paid holidays, parental leave, and more

• Wellness: Access to wellness programs and mental health support

• Professional Development: Opportunities for growth, including tuition reimbursement

Additional Perks:

• Flexible work arrangements, including remote work options

• Flexible Spending Accounts (FSAs)

• Employee referral programs

• Bonus opportunities

• Technology allowance

• A diverse, inclusive, and supportive workplace culture