AI Solution Architect

Job Description 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 Cloud–native 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 Cloud–native 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 patterns—specialized 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 Qualifications: BACHELOR OF COMPUTER SCIENCE Show more Show less