Founding Engineering Manager at Salla building and leading a new AI - focused team. Responsible for product innovation to maximize merchant value through AI - powered products.
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
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