Technical deep dives and system audits of generative AI solutions, LLM pipelines, autonomous multi-agent systems, and classical Data Science products within Schwarz Group companies.
Responsibilities
Conduct technical audits of generative AI solutions, LLM pipelines, autonomous multi-agent systems, and classical Data Science products.
Analyze source code, system architectures, and prompt strategies focusing on security, performance, and robustness.
Design, program, and optimize proprietary AI-assisted analysis and evaluation frameworks.
Analyze complex business processes and AI-driven workflows using BI and Process Mining tools (e.g., Celonis).
Support audit projects domestically and internationally, advising management and development teams.
Requirements
Successfully completed degree in Computer Science, Data Science, Mathematics, or comparable technical qualification.
Experienced Machine Learning Engineer with a background building machine learning and statistical modeling frameworks from scratch to optimize modeling processes and improve Swish products.
Compensation
Base salary: starting at $160,000 base plus bonus potential
Responsibilities
Design, prototype, implement, evaluate, optimize systems to generate sports datasets and predictions with high accuracy and low latency
Evaluate internal modeling frameworks and tools to optimize data scientist's modeling workflow
Build, test, deploy and maintain production systems
Work closely with DevOps and Data Engineering teams to assist with implementation, optimization and scale workloads on Kubernetes using CI/CD, automation tools and scripting languages
Support maintenance and optimization of cloud-native EDW and ETL solutions
Applying understanding of company products and services and introducing technology innovations with respect to functionality, performance, reliability, and scalability.
Designing, developing, and maintaining the machine learning infrastructure that powers the company.
Performing research and development of machine learning algorithms to expand product capabilities.
Participating in building new experiences, improving existing products, ensuring quality assurance test designs and contributing to test systems architecture discussions.
Requirements
Master’s degree in Computer Science, Engineering, Mathematics, or a related field
Lead the development of machine learning solutions that improve model performance and business outcomes, owning complex modeling problems from initial investigation through production implementation.
Locations (5)
Columbus, United States
Austin, United States
Bay Area, United States
New York City, United States
Canada
Compensation
United States | Remote - Anticipated Base Salary Range
$167,700—$231,800 USD
Canada | Remote - Anticipated Base Salary Range
$159,000—$193,000 CAD
Fine-tunes Large Language Models with emphasis on instruction fine-tuning and domain adaptation to enhance model relevance and performance in specific contexts.
Responsibilities
Perform independently and become an SME
Active participation/contribution in team discussions
Contribute in providing solutions to work related problems
Collaborate with team members to identify areas for model improvement and optimization
Conduct experiments to evaluate model performance and implement necessary adjustments
AI Architect with 6–9 years of overall IT experience, including 3–4 years of hands‑on experience in Artificial Intelligence, Machine Learning, or Generative AI solutions.
Responsibilities
Design end‑to‑end AI solutions across data, model, application, and infrastructure layers.
Translate business problems into AI‑driven technical architectures.
Define AI platform architecture, model deployment strategies, and integration patterns.
Evaluate and select appropriate AI/ML frameworks, tools, and technologies.
Provide architecture guidance for AI initiatives across the organization.
Requirements
Bachelor’s Degree in Engineering (Computer Science or Information Technology)
Nice to have: enterprise AI platform implementation
Nice to have: vector databases (Pinecone, FAISS, Weaviate, etc.)
Nice to have: AI observability and monitoring tools
Lead end-to-end development of machine learning models, from problem formulation and experimentation through deployment and performance evaluation
Design and evaluate advanced modeling approaches to improve predictive accuracy, robustness, and operational effectiveness
Identify opportunities to improve existing models through feature development, experimentation, and rigorous statistical analysis
Partner with engineering, product, and business stakeholders to translate complex requirements into measurable machine learning objectives
Guide technical decisions, review scientific methodologies, and share expertise to improve the quality and effectiveness of the team's work
Requirements
Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related field
PhD in mathematics, applied mathematics, statistics, physics, econometrics, operations research, computer science, or a related quantitative field (preferred)
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