emerchantpay

emerchantpay

emerchantpay specializes in providing seamless and secure online, mobile, and in-store payment processing solutions, along with risk and fraud management services, to help merchants enhance their conversion rates and expand their customer reach globally.

Diversified Financial Services
251-1K
Founded 2002

Description

  • Design, build, and maintain AI-powered applications, services, and integrations.
  • Implement AI agents, agentic workflows, automation, and LLM-based business processes.
  • Develop AI applications using Python frameworks, React frontends, and modern AI/ML tooling.
  • Build solutions on AWS AI/ML services, including Amazon Bedrock, AgentCore, and SageMaker.
  • Collaborate with the AI Tech Lead on architecture, technology choices, engineering standards, and rollout approaches.
  • Provide technical guidance on AI implementation patterns, testing, observability, code quality, and production readiness.
  • Develop AI agents that interact safely with internal APIs, business workflows, enterprise systems, knowledge bases, and external tools.
  • Build and maintain RAG solutions, including ingestion, chunking, embeddings, retrieval, reranking, and grounding.
  • Support machine learning model development, deployment, monitoring, evaluation, and lifecycle management.
  • Integrate LLMs via APIs and implement evaluation approaches for output quality, safety, reliability, and hallucination detection.
  • Support prompt engineering, function calling, tool use, memory patterns, guardrails, and LLM application testing.
  • Collaborate with product, engineering, data, DevOps, security, and business stakeholders to deliver practical AI solutions.
  • Write clean, maintainable, testable, and well-documented code.
  • Support production rollouts, troubleshooting, monitoring, optimization, and continuous improvement of AI systems.

Requirements

  • 7-8 years of professional experience in software engineering, AI engineering, ML engineering, data science, or related technical roles.
  • 2-3 years of experience in AI development, ML engineering, or data science with production deployment experience.
  • Strong hands-on experience building production-grade AI, ML, and data-driven systems.
  • Practical experience with AI agents, agentic workflows, LLM-based applications, tool-calling architectures, workflow automation, and AI orchestration patterns.
  • Strong understanding of deep learning, generative AI, LLMs, embeddings, RAG, LLM fine-tuning, and AI evaluation.
  • Strong Python development experience, including FastAPI, Flask, Django, or equivalent frameworks.
  • Some experience with React for user-facing AI tools, internal applications, dashboards, or workflow interfaces.
  • Strong knowledge of AWS, including Amazon Bedrock, Amazon Bedrock AgentCore, Amazon SageMaker, and related AI/ML services.
  • Experience with agent/orchestration frameworks such as LangChain, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or similar.
  • Experience with PyTorch or TensorFlow, and familiarity with Hugging Face Transformers.
  • Hands-on experience using LLMs via APIs such as OpenAI, Anthropic, Gemini, or similar providers.
  • Experience with ML pipelines and MLOps, including training, deployment, tracking, monitoring, evaluation, and production support.
  • Experience with AI evaluation frameworks and techniques for LLM outputs, RAG quality, agent behavior, safety, and reliability.
  • Knowledge or practical experience with RLHF, human-in-the-loop evaluation, preference data, reward modeling, or feedback-driven improvement.
  • Experience with vector databases and retrieval/search technologies such as Amazon OpenSearch, Pinecone, or pgvector.
  • Experience building RAG systems, including document ingestion, embeddings, retrieval evaluation, reranking, and grounding.
  • Experience with model fine-tuning, embedding models, transformer architectures, open-source LLMs, and model benchmarking.
  • Knowledge of API design, microservices, event-driven systems, and cloud-based architectures.
  • Good understanding of security and governance requirements for AI systems, including access control, secrets management, data privacy, and audit logging.
  • Experience working in cross-functional teams with engineers, product managers, data scientists, DevOps, security, and business stakeholders.
  • Strong problem-solving skills and ability to turn prototypes into reliable production systems.
  • Strong communication skills for technical and non-technical stakeholders.
  • Experience with Amazon Bedrock Agents, Bedrock Knowledge Bases, or Bedrock Guardrails is an advantage.
  • Experience with Docker and EKS/ECS is an advantage.
  • Experience with Terraform, AWS CDK, or CloudFormation is an advantage.
  • Experience with data platforms, ETL/ELT pipelines, data lakes, feature stores, or real-time data processing is an advantage.
  • Experience implementing responsible AI controls, AI governance frameworks, safety guardrails, and compliance processes is an advantage.
  • Experience with observability for AI systems, including tracing, cost monitoring, prompt/model analytics, latency tracking, and quality dashboards is an advantage.
  • Experience integrating AI systems with enterprise platforms, CRM/ERP systems, ticketing systems, knowledge bases, and workflow engines is an advantage.
  • Contributions to open-source AI/ML projects, technical publications, conference talks, or patents are an advantage.
  • AWS certifications in architecture, machine learning, security, or DevOps are an advantage.
  • Experience in fintech is an advantage.

Benefits

  • Fast-growing payment company environment.
  • Excellent working conditions with a casual atmosphere and state-of-the-art hardware.
  • Modern, challenging, and constantly growing business.
  • Professional development support, including books, trainings, and certifications.
  • Team buildings and fun activities.
  • 25 days paid holiday, plus 1 additional day for every 2 years with the company.
  • Fully distributed and remote work.

Interested in this position?

Apply directly on the company website

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