AI/ML Senior Engineer

Key Responsibilities

• Lead Agile ceremonies, including Sprint Planning, Grooming, and Estimation, with cross-functional teams comprising engineers, product managers, and data scientists to scope AI/ML, LLMOps, payment processing, and analytics solutions.
• Gather, analyze, and translate complex business requirements from stakeholders into scalable technical architectures aligned with enterprise goals.
• Define user journeys, data contracts, redline documents, and cross-platform integration requirements across web, mobile, backend, and payment systems.
• Architect enterprise-grade AI/ML platforms across AWS (e.g., SageMaker, Bedrock, Redshift) and GCP (e.g., Vertex AI, BigQuery) to support the full ML lifecycle.
• Design and implement scalable payment processing solutions, integrating payment gateways, payment APIs, and third-party financial service providers.
• Develop and integrate payment workflows covering authorization, capture, settlement, refunds, voids, recurring payments, and transaction reconciliation.
• Work with payment technologies and protocols, including REST APIs, webhooks, tokenization, and secure transaction processing, ensuring reliability and scalability.
• Ensure secure payment integrations and compliance with industry standards such as PCI DSS, along with appropriate data encryption, fraud prevention, and transaction monitoring mechanisms.
• Leverage AI/ML capabilities for payment fraud detection, anomaly detection, transaction risk assessment, and intelligent payment analytics.
• Build secure and production-ready LLMOps pipelines, including data preprocessing, RAG (Retrieval-Augmented Generation), fine-tuning, evaluation, deployment, and monitoring.
• Deploy and optimize large language models (e.g., GPT-4, Claude, Llama 3) using AWS Bedrock, GCP Vertex AI, and custom APIs.
• Integrate vector databases (e.g., Pinecone, FAISS, Chroma) and frameworks such as LangChain or LlamaIndex to support contextualized GenAI applications.
• Design and implement microservices for GenAI and payment processing use cases using Python (FastAPI, Flask), Node.js, and deploy via ECS Fargate and GKE.
• Build and operationalize real-time and batch data pipelines using AWS Glue, EMR, Kinesis, GCP Dataflow, and Pub/Sub for analytics, payment processing, and ML workflows.
• Integrate and govern data lakes and federated marts to support self-service analytics and compliance needs via tools such as Looker, Tableau, and Athena.
• Implement and manage CI/CD pipelines using GitHub Actions, Jenkins, and AWS/GCP DevOps tools to support model deployment and containerized workflows.
• Use Infrastructure as Code tools such as Terraform, AWS CDK, and Helm to provision and manage scalable cloud resources.
• Apply DevSecOps practices, including IAM, VPC segmentation, KMS, and compliance automation, to meet regulatory requirements (SOX, GDPR, HIPAA, and PCI DSS).
• Conduct comprehensive testing (unit, integration, load, and payment transaction testing) and monitor production performance using tools such as PyTest, Postman, Prometheus, CloudWatch, and Grafana.
• Establish model observability and monitoring frameworks (e.g., LangWatch, WhyLabs, Weights & Biases) to track performance, drift, and data quality.
• Mentor junior architects, ML engineers, and developers on best practices in MLOps, LLM architecture, payment integrations, and secure cloud deployments.
• Maintain comprehensive technical documentation, including architecture diagrams, SOPs, API specifications, and Confluence articles.
• Lead initiatives in GenAI innovation, payment technology modernization, observability tooling, and Responsible AI governance while ensuring platform security and compliance.

Preferred Payment Processing Experience:

• Hands-on experience integrating payment gateways such as Stripe, Adyen, Worldpay, PayPal, Braintree, or similar platforms.
• Strong understanding of payment lifecycle, transaction processing, authorization, settlement, refunds, chargebacks, and reconciliation.
• Experience with payment APIs, webhooks, tokenization, fraud detection, and PCI DSS compliance.

Compensation: $140,000.00 per year