Senior AI Engineer

Overview

Senior AI Engineer on the Operational Intelligence Program within Mastercard's Business & Market Insights (B&MI) group, designing, building and shipping production-grade agentic AI systems, and mentoring other engineers.

What you'll do

• Architect and build the tool-calling layer that powers agents, wrapping internal services, data platforms and external APIs as well-defined tools with clear schemas, contracts and permissions.
• Engineer reliable tool execution at scale, including parallel and chained tool calls, timeouts, retries, idempotency, error recovery, and graceful handling of tool failures or bad model outputs.
• Develop high-performance Python backend services for LLM orchestration, covering async job handling, streaming responses, caching, rate limiting and distributed workflows.
• Engineer state, memory and context management subsystems so agents can maintain session continuity across long, multi-step, tool-heavy interactions.
• Architect multi-agent and skills-based agent systems using frameworks such as LangGraph, CrewAI, AutoGen or similar.
• Build observability for agent behavior, including end-to-end tracing of tool calls, measuring tool-selection accuracy, and debugging multi-step agent runs in production.
• Build retrieval systems (RAG and Graph-RAG) using vector databases (e.g., Pinecone, pgvector, OpenSearch) and knowledge graphs (e.g., Neo4j, AWS Neptune).
• Establish LLMOps and MLOps pipelines on Databricks (AWS), covering MLflow, prompt and agent evaluation, model lineage tracking, CI/CD and monitoring.
• Design multimodal generative AI pipelines that unify text, image, tabular and graph data using transformer-based and foundation models.
• Guide fine-tuning, prompt engineering and alignment strategies (e.g., PEFT, LoRA, instruction tuning, RLHF) for specialized enterprise use cases, working closely with ML specialists.
• Implement Responsible AI practices, including hallucination mitigation, bias detection, output safety guardrails, explainability and compliance with data ethics standards.
• Evaluate and productionize advances in agentic AI, multimodal models and frontier foundation models, benchmarking them against enterprise performance and safety requirements.
• Lead, mentor and grow a team of engineers, and set the engineering standard for Gen AI development across the program.

What you'll need

• Bachelor's or Master's degree in Computer Science, AI/ML or Engineering, or equivalent practical experience.
• A strong track record of building and operating production backend systems at scale, including distributed services, APIs, async processing, data stores and observability.
• Strong API design and integration experience, building services that other systems and LLMs can call safely and reliably.
• Excellent Python engineering skills, including async programming, API development (FastAPI or similar), microservices, and SQL proficiency.
• Hands-on experience shipping at least one LLM-powered application to production that uses tool/function calling, such as agents that call APIs, query databases or trigger workflows, with OpenAI, Anthropic, Gemini or open-source models.
• A working understanding of Gen AI engineering patterns such as prompt design, embeddings, semantic search and agent memory, or a demonstrated ability to ramp up quickly on new technical domains.
• Experience with at least one cloud platform (AWS preferred; Azure or GCP welcome).
• Experience leading or mentoring engineers, plus strong communication and stakeholder management skills, and the ability to translate technical work into business value.

Nice to have

• Experience with Model Context Protocol (MCP) or building tool servers and connectors for LLM agents.
• Experience evaluating agent performance, such as tool-selection accuracy, task completion rates or multi-step reasoning reliability.
• Exposure to fine-tuning (LoRA, PEFT, RLHF), multimodal models, knowledge graphs, or MLOps tooling such as MLflow, Weights & Biases, SageMaker or Bedrock.

Details

• Location: Pune, India.