Own and drive the AI and automation strategy for the Spark Earnings Excellence team, delivering AI/ML solutions across enterprise scale.
Compensation
The annual salary range for this position is $110,000.00 - $220,000.00. Additional compensation includes annual or quarterly performance bonuses. Additional compensation for certain positions may also include : Stock
Responsibilities
Set and own the multi-year AI and automation vision across Spark Earnings Excellence pillars
Architect scalable data, platform, and governance foundations that enable AI at enterprise scale
Ship agentic AI for use cases like payment validation, defect root-cause analysis, reconciliation, anomaly detection, and audit reporting
Drive portfolio prioritization through rigorous business cases balancing impact, effort, and risk
Lead cross-functional Engineering, Data Science, and platform teams from concept to production with measurable outcomes
Requirements
Bachelor's in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field
Master's in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field
Nice to have: PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics
Build and deploy machine learning models for dexterity-driven physical AI robots, collaborating with researchers and engineers to integrate ML into production systems.
Responsibilities
Design, develop, and deploy machine learning models and algorithms to address specific business needs and challenges.
Implement scalable and efficient software solutions to integrate machine learning models into production systems.
Optimize and fine-tune machine learning algorithms for improved performance and accuracy.
Conduct thorough testing and validation of machine learning models to ensure robustness and reliability.
Provide technical guidance and mentorship to junior team members.
Requirements
Master’s degree or Ph.D. in Computer Science, Engineering, Mathematics, or a related field.
Lead measurement and insights work for generative music experiences, designing large-scale analyses, integrating evaluation signals, and building metrics and decision systems for Spotify's Artist-First AI Music lab.
Compensation
The United States base range for this position is $144,714–$206,735 USD, plus equity.
Responsibilities
Lead foundational measurement and insights work for generative music experiences
Design and run large-scale analyses and evaluations of generative music systems
Partner with Engineers and AI Researchers to integrate evaluation signals into model development loops
Identify behavioral and market dynamics that shape sustainable consumer demand and creator participation
Build practical, scalable metrics and decision systems that define what "good" looks like for AI music
Support enterprise AI/ML architecture initiatives, governance frameworks, and agentic AI capabilities across federal modernization programs.
Compensation
Salary Range: $120k - $155k
Responsibilities
Support AI/ML architecture design and engineering activities across enterprise architecture and modernization initiatives.
Assist in developing and maintaining enterprise AI architecture baselines, target-state architectures, and transition roadmaps.
Contribute to solution architecture efforts for AI-enabled systems, integrating cloud-native services and advanced analytics platforms.
Support the development and maintenance of AI model governance frameworks, including model lifecycle management, risk assessments, and compliance monitoring.
Assist in designing scalable data pipeline architectures to support AI/ML training, inference, monitoring, and continuous improvement.
Requirements
Bachelor’s degree in Computer Science, Engineering, Data Science, or related field
Master’s degree in Computer Science, Engineering, Information Technology, or related discipline
Nice to have: Master’s degree
Nice to have: AI/ML platforms and services including Azure AI, Azure Machine Learning, Amazon SageMaker, OpenAI services
Nice to have: Generative AI and large language model (LLM) technologies
Lead complex statistical modeling and machine learning initiatives within the Digital Solutions data team, defining workflows, enforcing quality standards, and integrating models into products and client solutions.
Responsibilities
Leads technical delivery of complex modeling and machine learning projects
Sets direction for modeling approach, evaluation criteria, and deliverables
Defines machine learning workflows, from feature pipelines and training through deployment and monitoring
Contributes to architecture decisions for model deployment and recommends system and tool improvements
Enforces model quality, reproducibility, documentation, and data governance standards
Requirements
Bachelor’s degree in Computer Science, Statistics, Mathematics, Data Science, Engineering, or a related field (or equivalent experience)
Nice to have: advanced degree
Nice to have: certifications in cloud data platforms or MLOps
Lead independent validation of AI agents and machine learning models in the financial sector, ensuring compliance, reliability, and readiness for production.
Responsibilities
Conduct independent Model Risk Validation assessments of AI agents and ML models
Review model design, architecture, methodology, assumptions, and performance
Evaluate model robustness, reliability, explainability, and potential limitations
Assess data quality, lineage, and supporting validation evidence
Review model documentation, governance frameworks, and lifecycle controls
Requirements
Nice to have: Model Risk Management frameworks (SR 11-7 or equivalent)
Nice to have: EU AI Act, GDPR, Responsible AI frameworks
Design and build AI/ML applications and systems, integrating cloud or on-prem pipelines, applying generative AI models, and leading cross‑functional teams to deliver production‑ready solutions.
Responsibilities
Lead design and implementation of AI‑driven solutions
Lead a globally distributed data science team to develop advanced analytics and machine learning solutions for Mastercard’s digital marketing services.
Responsibilities
Partner with global, cross‑functional teams to design, develop, and maintain advanced analytics and data science solutions
Propose and present creative solutions to existing challenges
Research and evaluate emerging AI, machine learning, and data science technologies
Measure, validate, implement, monitor and improve performance of machine learning models
Apply various machine learning and deep learning techniques to solve analytical problem statements
Requirements
Bachelor’s or Master’s degree in Computer Science, Data Science, Technology, Mathematics, Statistics, or a related field
The Manager, Data Scientist within the Optimization Solutions team focuses on enhancing digital payment performance across all Mastercard products and regions by designing and maintaining advanced analytics and machine learning model solutions.
Responsibilities
Partner with global, cross functional teams to design, develop, and maintain advanced analytics and data science solutions that drive business impact.
Build, deploy, and scale machine learning models that deliver actionable insights for product optimization, growth, and sales enablement.
Lead the end to end design and technical oversight of complex data science and ML solutions, ensuring scalability, performance, and engineering excellence.
Drive the development, validation, monitoring, and continuous improvement of predictive models, statistical techniques, and algorithms aligned with business objectives.
Establish and uphold best practices across statistical analysis, feature engineering, model evaluation, and performance monitoring.
CLIENT
Kforce/United Airlines
POSITION
GenAI Solutions Architect
LOCATION
Remote
DURATION
6 Month C2H
BILL RATE
$95/hr C2C
INTERVIEW TYPE
Video
VISA RESTRICTIONS
Must be able to convert without sponsorship
REQUIRED SKILLS
Required Qualifications
8+ years of experience in Software Engineering, Machine Learning, AI, or Solution Architecture roles.
Strong technical background in Machine Learning, NLP, Computer Vision, or related AI disciplines.
Hands-on experience architecting Generative AI solutions in enterprise environments.
Experience designing and implementing Retrieval-Augmented Generation (RAG) architectures.
Experience with Agentic AI frameworks and autonomous workflow orchestration.
Deep understanding of Large Language Models (LLMs) and prompt engineering methodologies.
Experience working within an AI Center of Excellence (CoE) or similar innovation-focused organization.
Proven track record of building successful AI POCs and advancing them to funded initiatives and production implementations.
Excellent communication, presentation, and stakeholder management skills.
Technical Requirements
Experience with several of the following technologies:
Large Language Models (OpenAI, Claude, Gemini, Llama, etc.)
RAG Architecture
Agentic AI Frameworks
Vector Databases:
Pinecone
Weaviate
Chroma
FAISS
Google Vertex AI
AWS SageMaker
Hugging Face
LangChain
LangGraph
LlamaIndex
Python
Machine Learning & Deep Learning Frameworks
Cloud Platforms (AWS, GCP, Azure)
API Design & Integration
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