Must Have Technical/Functional Skills
• Establishing enterprise GenAI/ML architecture from discovery PoC pilot production, ensuring scalability, security, reliability, and measurable business value. Experience in LLM, AI/ML Concepts.
• Partner with business, product, risk, and ops leaders to identify/prioritize AI use cases in payments, define success metrics, and build value/feasibility assessments.
• Design solution blueprints and produce HLD/LLD + Lucid architecture diagrams, covering integrations, NFRs, data flows, and deployment/run operations.
• Experience with On Prem as well as Cloudnative GenAI solutions (Google will be a Plus) using Vertex AI + Gemini, integrating with BigQuery/Cloud Storage and scalable runtime options (Cloud Run/GKE).
• Establish Prompt Engineering standards: system/tool prompts,few shot patterns, structured outputs (JSON schemas), guardrails, prompt versioning, and automated regression testing.
• Experience with Agentic AI frameworks like LangChain, LangGraph and LangSmith.
• Architect advanced RAG systems: ingestion pipelines, chunking/metadata strategy, hybrid retrieval + reranking, citation/grounding, and continuous quality evaluation.
Roles & Responsibilities
Lead end to end enterprise GenAI/ML architecture from discovery PoC pilot production, ensuring scalability, security, reliability, and measurable business value.
• Partner with business, product, risk, and ops leaders to identify/prioritize AI use cases in payments, define success metrics, and build value/feasibility assessments.
• Design solution blueprints and produce HLD/LLD + Lucid architecture diagrams, covering integrations, NFRs, data flows, and deployment/run operations.
• Experience with On Prem as well as Cloudnative GenAI solutions (Google will be a Plus) using Vertex AI + Gemini, integrating with BigQuery/Cloud Storage and scalable runtime options (Cloud Run/GKE).
• Establish Prompt Engineering standards: system/tool prompts, few shot patterns, structured outputs (JSON schemas), guardrails, prompt versioning, and automated regression testing.
• Experience with Agentic AI frameworks like LangChain, LangGraph and LangSmith.
• Architect advanced RAG systems: ingestion pipelines, chunking/metadata strategy, hybrid retrieval + reranking, citation/grounding, and continuous quality evaluation.
• Design vector data models and retrieval optimization (embeddings, indexing, freshness, governance) to support high accuracy, low latency enterprise knowledge experiences.
• Lead AI Agent design: tool/function calling, planning/execution loops, memory strategies, and human in the loop approvals for controlled automation.
• Build Agentic Workflow orchestration (multi step business processes) with clear role boundaries, fail safes, escalation paths, and auditability.
• Enable A2A (Agent to Agent) collaboration patternsspecialized agents (retrieval, policy, fraud signals, customer comms) coordinated via a central orchestrator.
• Define and govern MCP (Model Context Protocol) integrations to standardize tool connectivity, context injection, authorization, and safe tool execution across enterprise services.
• Drive MLOps/LLMOps practices: CI/CD, prompt/model versioning, automated evaluations, drift/quality monitoring, cost controls, canary releases, and rollback strategies.
• Embed payments grade security, privacy, and compliance: IAM least privilege, encryption/KMS, secrets management, PII controls, threat modeling, and audit evidence.
• Collaborate with internal teams and technology partners to ensure smooth implementation, performance tuning, and production readiness across environments.
• Mentor teams and evangelize an AI engineering culture through reusable reference architectures, best practices, knowledge sharing, and technical governance.
Salary Range: $100,000 to $130,000 per year
