Role Description The AI/ML Engineer – Work builds AI-driven workforce and skills-based capabilities for Cengage's career and learning products. You will develop the models and systems that infer skills, verify competencies, and power skills-based matching and recommendations — the capabilities that underpin Cengage's skills graph and workforce platforms including Skills Verification. This role requires a builder who is excited about applied ML for skills, career, and learning data. The ideal candidate has worked on matching, ranking, recommendation, or representation learning problems, understands the workforce and skills domain, and can ship production ML features that meaningfully improve learner career outcomes. Key Responsibilities - Skills & Workforce AI Development - Develop skills inference models that extract competencies from content, assessments, and learner activity - Build skills verification models powering the Skills Verification platform - Create skills-based matching and recommendation systems for jobs, courses, and learning paths - Develop career pathway recommendation and skills gap analysis features - Integrate AI into Cengage workforce platforms including Infosec Skills and IQ - Platform Integration & Engineering - Integrate AI into workforce platforms including content, assessment, and lab systems - Enable skills-based matching and recommendations across Cengage's workforce ecosystem - Partner with platform engineering on API design, scaling, and production deployment - Align to the NICE Framework and other recognized skills taxonomies where applicable - Build evaluation and monitoring systems to measure and improve model accuracy and performance - Measurement & Business Impact - Track skills verification accuracy, recommendation quality, and adoption of AI-driven career tools - Partner with product and data science on offline and online evaluation - Drive integration completeness across Cengage's workforce product suite - Maintain feature delivery cadence with weekly shipping discipline - Partner with Governance on patent and IP considerations for novel approaches Qualifications - Bachelor's degree in Computer Science, Engineering, or related field - 4+ years of experience in software engineering, with at least 2 years focused on AI/ML - Strong proficiency in Python with experience building production ML systems - Hands-on experience with modern ML techniques including embeddings, ranking, and LLMs - Experience with recommendation systems, matching, or representation learning - Solid software engineering fundamentals including testing, CI/CD, and system design - Experience with offline and online model evaluation - Strong communication skills to work with product, data science, and platform teams Preferred Qualifications - Prior experience in workforce tech, HR tech, or skills-based matching platforms - Familiarity with skills taxonomies (NICE Framework, O*NET, ESCO, Lightcast) - Experience with LLMs for classification, extraction, and zero-shot matching - Background in cybersecurity education or adjacent technical learning domains - Experience with behavioral pattern analysis or competency assessment - Familiarity with agentic AI frameworks (LangChain, LlamaIndex) Tools & Technologies - Languages: Python, JavaScript/TypeScript, SQL - AI/ML: PyTorch, TensorFlow, Hugging Face, OpenAI API, Anthropic API - ML Infra: AWS SageMaker, MLflow, Weights & Biases, Ray - Vector DBs: Pinecone, Weaviate, pgvector - Data: Snowflake, Databricks, Postgres, Spark - DevOps: Docker, Terraform, GitHub Actions, CI/CD pipelines Key Competencies - ML Craft — fluent across classical ML and modern LLM-based approaches - Product Orientation — connects model improvements to learner and user outcomes - Shipping Mindset — delivers on weekly cadence with measured impact - Domain Curiosity — invests in understanding skills, workforce, and career dynamics - Evaluation Rigor — designs offline and online evaluation thoughtfully - Collaboration — partners effectively with product, research, and platform engineering Benefits - Opportunity to shape AI at scale across a global learning company - Direct impact on business outcomes, product, and workforce productivity - Access to cutting-edge AI tools, platforms, and technologies - Collaborative team environment focused on innovation and continuous improvement - Competitive compensation and comprehensive benefits - Professional development and learning opportunities - Flexible work arrangements with remote/hybrid options

Role Description Pantheon Data is seeking a Machine Learning Engineer to join our core product team. This role focuses on the development and maintenance of Intelligent Document Processing (IDP) and Generative AI solutions within secure federal environments. The ideal candidate possesses strong Python programming skills and hands-on experience deploying workloads in AWS. You will work alongside senior architects to build and support AI pipelines that process unstructured data for mission-critical federal applications, specifically leveraging managed AI services. Responsibilities - Python Development: Engineering clean, production-ready Python code for data ingestion, preprocessing, and model orchestration. - AWS Bedrock Integration: Building and maintaining Generative AI workflows using AWS Bedrock for model invocation, embedding generation, and RAG-based implementations. - AWS Implementation: Deploying and managing ML models using Amazon Bedrock, AWS Textract, and AWS Lambda within federal cloud boundaries. - Pipeline Support: Building and optimizing automated pipelines for OCR and NLP workflows to enhance document intelligence. - Data Engineering: Managing data transformations and semantic search indexing using SQL/NoSQL and vector-enabled databases. - Security & Compliance: Implementing security controls and data-at-rest encryption to meet FedRAMP and NIST standards within AWS GovCloud. - Collaboration: Participating in Agile sprints to deliver iterative updates to Pantheon's core AI products. Qualifications - AWS Experience: Minimum 4+ years of hands-on experience deploying solutions on AWS. - Proficiency with Docker, EC2, S3, IAM, and Lambda is required. - AWS Bedrock Proficiency: Practical experience using AWS Bedrock to implement Large Language Model (LLM) solutions. - Python Proficiency: Advanced skills in Python and common ML libraries (Pandas, NumPy, Scikit-learn). - ML Fundamentals: Basic understanding of the machine learning lifecycle, specifically regarding NLP and OCR technologies. - Technical Stack: Experience with Linux environments, Git version control, and Docker containerization and EKS. - Ability to work effectively remotely in cross-functional teams. - Ability to meet deadlines and produce quality work. - Proficient in Microsoft Suite software including Outlook, Word, Excel, SharePoint, and PowerPoint. Preferred Skills and Experience - AWS GovCloud: Demonstrated experience operating within AWS GovCloud. - Infrastructure as Code (IaC): Basic knowledge of Terraform and/or CloudFormation for provisioning cloud resources. - Federal Environments: Previous experience developing code for government-hosted or highly regulated environments. - Certifications: AWS Certified Solutions Architect – Associate, or AWS Certified Machine Learning – Specialty. Clearance Requirements - U.S. Citizenship with the ability to obtain and maintain a DOD Secret clearance. Work Location - United States - Remote - Our company prioritizes the benefits of flexibility and collaboration, whether that happens in person or remotely. - If the position is remote or hybrid, you may periodically work from a Pantheon Data office location or client site. - If this position is assigned to a Pantheon Data office location or client site, you'll work with colleagues and clients in person, as needed for specific client requirements. Compensation The salary range for this position is $110,000 - $140,000. This is not, however, a guarantee of compensation or salary. Rather, salary will be set based on experience, geographic location and possibly contractual requirements and could fall outside of this range. Benefits - Pantheon Data is committed to providing its employees with competitive salaries and benefits in order to increase employee satisfaction and productivity. - In addition to our benefits, we also offer SmartBenefits through the Washington Metro Area Transportation Authority, where you specify an amount of your pre-tax wages be paid directly to your SmarTrip account. - In some cases, tuition assistance may be available for continuing education expenses and certifications related to their position.

• Work directly on building, deploying, and iterating on machine learning models and agentic workflow features that address real customer needs • Apply ML techniques to improve accuracy and overall system performance, ensuring solutions are robust, reliable, and production-ready for customers • Improve, implement, and validate ML models and agentic workflows supporting submission intake, underwriting decision-making, and automation tasks • Deploy and adapt autonomous agent behaviors into customer-specific workflows, translating core AI capabilities into practical solutions • Develop and maintain evaluation pipelines, monitoring systems, and performance metrics to ensure reliability under evolving production conditions • Monitor production systems via logs, metrics, and user feedback to diagnose issues, debug failures, and drive resolution • Take end-to-end ownership of problems — implementing fixes or coordinating with engineering and infrastructure teams as needed • Partner closely with Data Science and Engineering teams to iterate quickly and deliver high-impact solutions

• Lead a team of senior data scientists focused on fine-tuning large language models • Collaborate with senior leadership to define the team roadmap and align priorities • Lead weekly meetings and standups, keeping the team unblocked • Provide technical direction across projects using open-weight and off-the-shelf LLMs • Oversee experimentation, optimization, and data quality to ensure models are production-ready • Foster creative problem-solving and methodological rigor to address challenges • Translate complex model outputs into actionable insights for stakeholders