Description

The AI Engineer at VizyPay will lead the design, development, deployment, and governance of enterprise AI solutions. This involves shaping the AI strategy, building a scalable AI platform, and embedding production-grade AI capabilities into VizyPay's CRM platform and business systems. The role focuses on ensuring AI safety, measurability, audit-readiness, resilience, and cost-effectiveness, particularly within a security- and compliance-driven payments environment.

Required Skills

AI
Python
TypeScript
SQL
PostgreSQL
MySQL

Additional Information

Required Previous Experiences: 7+ years of professional software engineering experience
3+ years designing, building, and operating production ML/AI systems at enterprise scale
Preferred Qualifications: AI/ML engineering certifications: AWS Certified Machine Learning – Specialty, Microsoft Azure AI Engineer Associate (AI-102), or Databricks Generative AI Engineer Associate
AI governance and security certifications: IAPP AI Governance Professional (AIGP), ISO/IEC 42001 Lead Implementer, or ISACA Advanced in AI Audit (AAIA)
Experience establishing an AI function, platform, or practice from the ground up (0->1) in an organization without prior AI infrastructure.
Experience in security- or compliance-constrained environments (e.g., PCI DSS, SOC 2, or financial services regulation), delivering under formal SDLC and change management.
Strong SQL and production relational databases (e.g., PostgreSQL, SQL Server, MySQL) with in-database vector search; ETL/ELT pipelines, data modeling, and data quality to make enterprise data AI-ready.
Technical knowledge in Python and/or TypeScript, API design, event-driven integration (REST, webhooks, queues/streaming), cloud-native services (AWS, Azure), containers, serverless/edge compute (e.g., Lambda, Cloudflare Workers), and infrastructure-as-code (e.g., Terraform).
Strong understanding of classical machine learning: supervised and unsupervised techniques (classification, regression, clustering, anomaly detection) with disciplined model validation.
RAG architectures, embeddings, and vector databases (e.g., pgvector, Pinecone, Weaviate, Qdrant, OpenSearch), prompt engineering and versioning, structured outputs, function/tool calling, and multi-step agentic orchestration.
LLM observability and evaluation platforms (e.g., Langfuse, LangSmith, Arize Phoenix), model lifecycle tooling (e.g., MLflow, Weights & Biases), and CI/CD for AI systems (e.g., GitHub Actions).
OCR and structured extraction (e.g., Azure Document Intelligence, AWS Textract, Google Document AI, or LLM-based extraction pipelines).
OAuth 2.0/OIDC and service-to-service authentication, vault-based secrets management, and RBAC design for AI tools and data access.
Proven ability to translate ambiguous business problems into shipped AI capabilities with measurable outcomes, and to present strategy, risk, and tradeoffs to executive stakeholders.
Track record of technical leadership: mentoring, architecture review, standards ownership, or team leadership.
Experience in payments, fintech, banking, or other regulated financial industries; integrating AI with SaaS business systems (e.g., HubSpot, Microsoft 365/Graph API, QuickBooks).
Building and securing MCP servers and tools; designing multi-agent systems (e.g., LangGraph or comparable orchestration frameworks).
Fine-tuning, distillation, or inference optimization (e.g., LoRA/PEFT, quantization, vLLM); red-teaming LLM applications; AI governance aligned to recognized frameworks (NIST AI RMF, ISO/IEC 42001).

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AI Engineer at 6d766615-e0a5-4e5e-a433-e361e3fbd5e1

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About 6d766615-e0a5-4e5e-a433-e361e3fbd5e1

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About 6d766615-e0a5-4e5e-a433-e361e3fbd5e1