Senior AI Engineer
艾默生特支付是全球领先的在线，移动，商店和电话支付服务提供商和收购商。我们的全球支付解决方案通过简单的集成可用，提供了各种功能，包括全球收购，全球和本地支付方法，高级欺诈管理和绩效优化。我们使企业能够为消费者设计无且有吸引力的支付体验。我们正在寻找一名高级 AI 工程师，加入我们的人工智能工程团队并帮助设计，构建和推出生产级的 AI 解决方案，重点集中在人工智能技术，人工智能代理，机器学习，GenAI 和 LLM 基于应用程序。这是人工智能 इंजीनियरिंग团队内的高级个人贡献者角色。高级 AI 工程師将与人工智能科技领导，工程团队，产品利益相关者，数据团队，云/平台团队以及安全团队密切合作，为真正的业务系统提供可靠的 AI 能力。技术堆多样化，可以包括 Python (FastAPI/Flask/Django) 或相等框架；反应在前端，以及各种 ML/AI 框架，API,云本土服务和现代人工智能工具。该角色将重点关注 AWS,包括亚马逊贝德罗克，亚马逊 BEDROCK 代理 Core,亚马遜 SageMaker 和其他 AWSAI/ML 服务。责任 ·作为 AI 工程团队的一部分设计，构建和维护基于 AI 的应用程序，服务和集成。·实现解决方案集中于 AI 代理，机器人工作流程，自动化，LLM 基础上的应用程序和 AI 辅助业务过程。·使用 Python (FastAPI/Flask/Django) 或相等技术，React 前架构和相关的 ML 框架来建立和集成 AI 应用程序。· 通过 AWS AI/ML 服务实现 AI 解决方案，包括亚马逊 Bedrock,Amazon Bedrock AgentCore,Amazon SageMaker 和其他 AWS 服务用于模型托管，推理，调整，数据处理，监控和安全。· 与人工智能技术领导人员密切合作，以协调建筑，技术选择，工程标准，人工智能模式和部署方法。· 向其他工程师提供有关人工智能实施模式，代码质量，测试，可观察性和生产准备的技术输入和指导。· 在一个过程中开发并集成与内部 API,业务工作流程，企业系统工具，知识基础以及外部互动的 AI 代理。
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emerchantpay is a leading global payment service provider and acquirer for online, mobile, in-store and over the phone payments. Our global payments solution is available through a simple integration, offering a diverse range of features, including global acquiring, global and local payment methods, advanced fraud management and performance optimisation. We empower businesses to design seamless and engaging payment experiences for their consumers.
We are looking for a Senior AI Engineer to join our AI Engineering team and help design, build, and roll out production-grade AI solutions, with a strong focus on AI engineering, AI agents, agentic workflows, machine learning, GenAI, and LLM-based applications.
This is a senior individual contributor role within the AI Engineering team. The Senior AI Engineer will work closely with the AI Tech Lead, engineering teams, product stakeholders, data teams, cloud/platform teams, and security teams to deliver reliable AI capabilities into real business systems.
The technology stack is diverse and can include Python (FastAPI/Flask/Django) or equivalent frameworks; React on the frontend side, and various ML/AI frameworks, APIs, cloud-native services, along with modern AI tooling.
The role will have a strong focus on AWS, including Amazon Bedrock, Amazon Bedrock AgentCore, Amazon SageMaker, and other AWS AI/ML services.
Responsibilities
- Design, build, and maintain AI-powered applications, services, and integrations as part of the AI Engineering team.
- Implement solutions focused on AI agents, agentic workflows, automation, LLM-based applications, and AI-assisted business processes.
- Build and integrate AI applications using technologies such as Python (FastAPI/Flask/Django) or equivalent frameworks, React frontends, and relevant AI/ML frameworks.
- Implement AI solutions using AWS AI/ML services, including Amazon Bedrock, Amazon Bedrock AgentCore, Amazon SageMaker, and other AWS services for model hosting, inference, orchestration, data processing, monitoring, and security.
- Work closely with the AI Tech Lead to align on architecture, technology choices, engineering standards, AI patterns, and rollout approaches.
- Provide technical input and guidance to other engineers on AI implementation patterns, code quality, testing, observability, and production readiness.
- Develop and integrate AI agents that interact with internal APIs, business workflows, enterprise systems, knowledge bases, and external tools in a safe and controlled way.
- Build and maintain RAG-based solutions, including document ingestion, chunking, embeddings, vector search, retrieval logic, reranking, and grounding techniques.
- Support the development and deployment of machine learning models and AI solutions into production environments.
- Contribute to ML pipelines and MLOps practices, including data preparation, model training, experiment tracking, model deployment, monitoring, evaluation, and lifecycle management.
- Integrate LLMs through APIs.
- Implement AI evaluation approaches for LLM outputs, RAG quality, agent behavior, model performance, hallucination detection, safety, and reliability.
- Support prompt engineering, prompt versioning, function calling, tool use, memory patterns, guardrails, and LLM application testing.
- Design and consume APIs and contribute to cloud-based, scalable backend architectures.
- Collaborate with product managers, engineers, data scientists, 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.
- Stay current with modern AI technologies, frameworks, models, and engineering practices, and bring practical recommendations to the team.
Requirements
- Minimum 7-8 years of professional experience in software engineering, AI engineering, ML engineering, data science, or related technical roles.
- At least 2-3 years of experience in AI development, ML engineering, or data science, with a demonstrated track record of deploying machine learning models and AI solutions in production environments.
- 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 modern AI concepts, including deep learning, generative AI, LLMs, embeddings, RAG, LLM fine-tuning, and AI evaluation.
- Strong Python development experience, including experience with Python (FastAPI/Flask/Django) or equivalent frameworks.
- Some experience with React for building user-facing AI tools, internal applications, dashboards, or workflow interfaces.
- Strong knowledge of AWS, including practical experience with cloud-native architectures, Amazon Bedrock, Amazon Bedrock AgentCore, Amazon SageMaker, and related AWS AI/ML services (the more, the better)
- Experience with advanced LLM frameworks such as LangChain, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or similar agent/orchestration frameworks.
- 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 data preparation, model training, model deployment, experiment tracking, model/version management, monitoring, evaluation, and production support.
- Experience with AI evaluation frameworks, tools, and techniques for assessing LLM outputs, RAG performance, agent behavior, model quality, safety, reliability, and regression over time.
- Knowledge or practical experience with RLHF - human-in-the-loop evaluation, preference data, reward modeling, or feedback-driven model improvement.
- Experience with vector databases and retrieval/search technologies, such as Amazon OpenSearch, Pinecone, pgvector, or similar.
- Experience building RAG systems, including document ingestion, chunking strategies, embeddings, retrieval evaluation, reranking, and grounding techniques.
- 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, audit logging, and safe handling of sensitive data.
- 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 AI prototypes into reliable, maintainable production systems.
- Strong communication skills and ability to explain technical decisions clearly to both technical and non-technical stakeholders.
Considered as an Advantage
- Experience with Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, Amazon Bedrock Guardrails, or similar managed AI capabilities.
- Experience with containerization and orchestration, including Docker and EKS/ECS.
- Experience with infrastructure as code using Terraform, AWS CDK, or CloudFormation.
- Experience with data platforms, ETL/ELT pipelines, data lakes, feature stores, and real-time data processing.
- Experience implementing responsible AI controls, AI governance frameworks, safety guardrails, and compliance processes.
- Experience with observability for AI systems, including tracing, cost monitoring, prompt/model analytics, latency tracking, and quality dashboards.
- Experience integrating AI systems with enterprise platforms, internal APIs, CRM/ERP systems, ticketing systems, knowledge bases, and workflow engines.
- Contributions to open-source AI/ML projects, published technical content, conference talks, or patents in AI/ML-related areas.
- AWS certifications, especially in architecture, machine learning, security, or DevOps.
- Experience in fintech.
Benefits
- Fast-growing payment company;
- Excellent working conditions, casual atmosphere, and state-of-the-art hardware;
- Modern, challenging, constantly growing business;
- Professional development – books, trainings, certifications, etc.;
- Team buildings and fun activities;
- 25 days paid holiday, 1 day for every 2 years with us;
- Fully distributed and remote.
If you are interested, please apply with your CV in English only. Only short-listed candidates will be contacted.
Personal data of the applicants will be processed in strict confidentiality by emerchantpay ltd. UIC 175117520 solely for the purposes of selection and recruitment and will not be transferred to other data controllers unless required by law. Applicants provide their personal data on a voluntary basis and will have the right to access and correct their personal data within a reasonable time upon filing a written request.
emerchantpay is an equal opportunity employer. We appreciate people with different backgrounds and mindsets, and we honor diversity and inclusion.
Originally posted on Himalayas