AIML Engineer Skill Level 1

TO BE CONSIDERED FOR THIS POSITION YOUMUST CURRENTLYHAVE AN ACTIVETS/SCI WITH POLYGRAPH SECURITY CLEARANCE WITH THE FEDERAL GOVERNMENT. (U.S. CITIZENSHIP REQUIRED).

Description:
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.

Seeking an AI/ML Level 1 Engineer responsible for building training fine-tuning and evaluating advanced language models. The ideal candidate will have hands-on experience with state-of-the-art machine learning techniques specifically for natural language processing (NLP) and will also have a strong understanding of full-stack development. This person will work across the model development lifecycle and collaborate with cross-functional teams to deliver high-impact solutions.

Key Responsibilities:
Knowledge and experience of Language models is required
o Specific experience with Marian and multi-lingual model development
Knowledge of Computation Linguistics is a plus
Experience with Open-Source model libraries Deep Learning Containers (DLC) GPU technologies and optimization / tuning
Strong Java C C programming experience
Willingness to support occasional on-call duties is a plus
Model Development & Training:
o Build train and fine-tune machine learning models particularly language models
o Apply best practices in model training tuning and optimization.
o Design and implement solutions for model performance improvement.
Evaluation & Testing:
o Conduct rigorous model evaluation including performance analysis and benchmarking.
o Perform error analysis debugging and model diagnostics to ensure quality and reliability.
Model Deployment & Integration:
o Work with cloud-based AI platforms (especially AWS Sagemaker) to deploy and scale models.
o Integrate machine learning models into production environments ensuring seamless integration with other systems.
Full-Stack Engineering:
o Contribute to the development and maintenance of the full stack for AI model-based applications (front-end and back-end).
o Collaborate with software engineers to build scalable and efficient deployment pipelines.
Collaboration & Reporting:
o Work closely with data scientists product teams and engineers to translate business requirements into technical solutions.
o Document workflows model design processes and technical specifications.

Key Qualifications:
Technical Skills:
o Strong proficiency in AI/ML frameworks such as TensorFlow PyTorch or Hugging Face.
o Expertise in model training evaluation and deployment.
o Hands-on experience with AWS tools like SageMaker Lambda EC2 and S3.
o Experience in developing full-stack software applications (JavaScript Python Java etc.).
o Solid understanding of data structures algorithms and system design.
Experience & Background:
o Extensive experience in machine learning model development including natural language processing (NLP).
o Experience with model evaluation optimization and performance monitoring.
o Proven experience in software engineering with strong coding skills.
o Experience working in a government or defense-related environment is highly preferred.
Additional Skills:
o Experience with model versioning and management using tools such as MLFlow or Git.
o Familiarity with containerization tools (i.e. Kubernetes).
o Strong problem-solving and analytical skills.
o Excellent communication and collaboration abilities.
Nice to Have:
Experience with secure data handling and compliance requirements
Experience working with Large Language Models (LLMs)

Polygraph must be within 7 years.
2 years experience in applied machine learning in programs and contracts of similar scope type and complexity is required. A B.S. degree in advanced math (e.g. calculus linear algebra or Bayesian statistics) computer science or related STEM discipline from an accredited college or university is required. 3 years of additional machine learning experience on projects with similar machine learning processes may be substituted for a bachelors degree.

Compensation:
We are committed to providing fair and competitive compensation. The salary range for our positions vary depending on accepted contractual position skill level. These salaries fall within the range of $78000 to $275000 per year. This range reflects the compensation offered across the locations where we hire. The exact salary will be determined based on the candidates 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

• 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