Lead AI Designer- Agentic AI (Maharashtra)

Title and Summary Lead Agentic AI Designer Developer(L6) Agentic AI Product Engineering, Operational Intelligence Mastercard Services Locations: Gurgaon, India | Pune, India Overview Role
- Design and develop intelligent AI systems capable of reasoning, diagnostics, anomaly detection, workflow automation, and operational recommendations across Mastercard's Operational Intelligence product suite.
- Build modular LLM-driven workflows for planning, retrieval, summarization, tool execution, classification, and autonomous decision support.
- Develop stateful and deterministic execution flows with routing logic, fallback handling, confidence scoring, guardrails, retry mechanisms, and human-in-loop intervention.
- Engineer reusable AI orchestration services using frameworks such as LangGraph, LangChain, OpenAI Agent SDK, AutoGen, Strands, AgentCore, Semantic Kernel, or equivalent technologies.
- Design and implement contextual memory systems including session memory, long-term memory, semantic/vector memory, episodic memory, and graph-based relationship memory.
- Build enterprise grounding and retrieval capabilities leveraging RAG, Graph RAG, structured retrieval, enterprise APIs, and deterministic tool-based integrations.
- Develop Python/FastAPI based backend services, APIs, and microservice components to support enterprise-scale AI workflow execution.
- Build secure integrations with operational data stores, event streams, internal systems, enterprise services, and external model endpoints.
- Partner with Product Management and domain SMEs to convert customer problems and product use cases into scalable technical AI workflows and reusable product components.
- Collaborate with UX and frontend teams to design trustworthy AI interaction experiences including evidence layers, confidence indicators, decision traces, action recommendations, and audit visibility.
- Build rigorous evaluation and testing frameworks to measure model quality, hallucination rates, retrieval quality, workflow success, latency, reliability, and operational cost.
- Mentor junior engineers and contribute to reusable engineering standards, internal frameworks, and best practices for Mastercard's growing AI-native product portfolio. All About You
- Strong software and backend engineering foundation with 7+ years of experience building scalable enterprise applications or AI-driven systems.
- 3+ years of hands-on experience building LLM applications, intelligent automation workflows, autonomous agents, or enterprise AI reasoning systems.
- Proven experience taking at least one AI/ML or LLM-based product from prototype to production deployment.
- Strong hands-on development expertise in Python, APIs, distributed systems, asynchronous services, and microservice-based architectures.
- Experience working with agent orchestration or LLM application frameworks such as LangGraph, LangChain, OpenAI Agent SDK, AutoGen, Strands, Semantic Kernel, AgentCore, or similar technologies.
- Hands-on experience with memory and retrieval systems such as Redis, Postgres, Pinecone, LanceDB, Mem0, Neo4j, Elasticsearch, or equivalent platforms.
- Experience building RAG, Graph RAG, or grounded enterprise retrieval workflows.
- Familiarity with enterprise deployment patterns using Docker, Kubernetes, CI/CD pipelines, and cloud environments such as AWS or Azure.
- Experience with evaluation, benchmarking, and observability tooling for AI applications such as Promptfoo, LangSmith, OpenTelemetry, Grafana, Signoz, or equivalent.
- Solid understanding of building AI systems with deterministic fallback, reliability controls, explainability, and human oversight.
- Ability to work across product, engineering, UX, domain SMEs, and platform teams in a highly collaborative environment.
- Strong problem-solving mindset with demonstrated ability to operate in a fast-moving product development setting. Preferred Qualifications
- Prior experience in payments, reconciliation, fraud, compliance, financial operations, or operational intelligence platforms.
- Experience designing context-aware or stateful autonomous systems.
- Exposure to Graph RAG, knowledge graphs, or advanced memory orchestration.
- Familiarity with production-grade AI observability, agent performance tuning, and LLM cost optimization.
- Experience contributing to reusable internal AI frameworks or platform accelerators. Why This Role Matters This role will help define how Mastercard builds the next generation of AI-native operational products moving beyond traditional analytics into intelligent systems that can reason, guide, and automate complex operational workflows for customers globally. Apply on Kit Job: kitjob.in/job/4ndpeg