Hybrid AI Engineering Manager

at TASC

• Job title: AI Engineering Manager
• Job type: Full Time
• Experience level: Mid level, Senior
• Salary: $138,000 - $265,000 per year
• Degree requirement: Bachelor's Degree
• Tech skills: Microservices, Python, PyTorch, Tensorflow
• Location requirements: Hybrid, San Francisco, United States

About the role

• AI Engineering Manager leading AI and ML solutions at Mastercard with technical depth and people leadership. Own delivering from architecture to scalable deployment with a high performing engineering team.

Responsibilities

• Hire, develop, and retain a high performing team of AI engineers (LLM/ML, full stack, platform/MLOps, LLMOPs, evals) with clear growth paths, coaching, and inclusive practices.
• Establish engineering rituals (design reviews, postmortems, chapter forums) and uphold high bars for code quality, testing, security, and documentation.
• Define technical strategy and reference architectures for Agentic AI solutions and traditional AI/ML solutions
• Guide teams from POC to production: requirements, solution design, backlog, sprint execution, integration, performance, and operational readiness.
• Drive platform thinking—build reusable Agentic AI services, SDKs, and patterns for retrieval, orchestration, guardrails, evaluation, and observability.
• Lead design and build of Agentic AI solutions for priority business workflows across all Mastercard’s Business
• Implement RAG, function/tool calling, knowledge graph integrations, and domain adapters for enterprise contexts.
• Stand up evaluation frameworks (offline/online, human in the loop) for quality, safety, latency, and task success, champion prompt and policy versioning.
• Own CI/CD for models and prompts, feature stores, vector indices, and model/prompt registries.
• Ensure observability, content safety, and guardrails in production.
• Partner with data engineering on pipelines, Legal and Data & AI Governance teams for data contracts, and Data product managers for high quality, policy compliant datasets.
• Embed privacy by design, data minimization, and financial services grade security into architectures.
• Collaborate with Risk, Compliance, and Legal to meet obligations (e.g., PCI DSS, GDPR, SOC 2, ISO 42001), and to operationalize Responsible AI (transparency, fairness, human oversight, auditability).
• Establish model risk management processes.
• Partner with Product Managers to define outcomes, prioritize roadmaps, and validate user value through experimentation.
• Translate complex technical tradeoffs for non-technical stakeholders, influence investment decisions with clear ROI and risk framing.
• Drive enablement for internal customers and ensure measurable adoption.
• Plan for multi region, high availability deployments with disaster recovery, performance tuning, and cost optimization.

Requirements

• Bachelor’s or Master’s in Computer Science, Data Science, or related field (or equivalent practical experience)
• Highly experienced background in software/AI engineering, including multiple years managing engineering teams delivering production AI/ML or Agentic AI systems
• Proven track record shipping enterprise grade AI solutions at scale (high availability, low latency, strong security, and compliance)
• Languages/Frameworks: Python, PyTorch/TensorFlow; modern microservices
• GenAI/LLMs: Prompt engineering, RAG, function/tool calling, agent frameworks, vector databases, embeddings
• Data & Platforms: Modern data stacks, event driven designs; experience with one or more major clouds and GPU/accelerator workflows
• MLOps/LLMOps: CI/CD, model & prompt registries, feature stores, model serving, canary/AB, offline/online evals, observability, cost management
• Security & Responsible AI: Secrets management, IAM, network isolation, policy enforcement; familiarity with content safety/guardrail tooling and Responsible AI practices
• UX Collaboration: Ability to partner with design and research on human centered, accessible interfaces for AI infused workflows
• Experience in payments/financial services or similarly regulated environments is highly preferred
• Knowledge of PCI DSS, GDPR, ISO 42001, and model risk practices; prior work with sensitive data controls (PII, tokenization, redaction)
• Multi region/active deployments, SLA/SLO design, and incident response leadership
• Vendor/platform evaluation and contract oversight for AI tooling and foundation models
• Publications, patents, OSS contributions in ML/LLM/agents are a plus.

Benefits

• insurance (including medical, prescription drug, dental, vision, disability, life insurance)
• flexible spending account and health savings account
• paid leaves (including 16 weeks of new parent leave and up to 20 days of bereavement leave)
• 80 hours of Paid Sick and Safe Time
• 25 days of vacation time and 5 personal days, pro-rated based on date of hire
• 10 annual paid U.S. observed holidays
• 401k with a best-in-class company match
• deferred compensation for eligible roles
• fitness reimbursement or on-site fitness facilities
• eligibility for tuition reimbursement
• many more