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Hybrid AI Engineering Manager

at TASC
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Posted 2 weeks ago

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About the role

  • 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

Job title

AI Engineering Manager

Job type

Full Time

Experience level

Mid levelSenior

Salary

$138,000 - $265,000 per year

Degree requirement

Bachelor's Degree

Tech skills

MicroservicesPythonPyTorchTensorflow

Location requirements

HybridSan FranciscoUnited States

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