# Senior AI Engineer **Company:** [emerchantpay](http://jobs.workable.com/companies/53YKQxWtxpftakxHP1udNR.md) **Location:** Remote **Workplace:** remote **Department:** IT/AI [Apply for this job](http://jobs.workable.com/view/047a9aa2-fa7a-49b6-a75f-cc9eb0a2b80f) ## Description 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 (t**he 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.