Senior AI Engineer

Remote from: Bulgaria
Annual salary: Undisclosed
Salary information is not provided for this position. Check our Salary Directory to estimate the average compensation for similar roles.
Department: Software Engineering
Employment type: Full Time,
Job posted: 18 May 2026
Apply before: 18 Jun 2026
Experience level: Senior
Views / Applies: 11 / 3

A trusted payment service provider offering global and local payment solutions since 2002.

Financial Services
2002

AI Summary

This Senior AI Engineer role at emerchantpay involves designing, building, and maintaining production-grade AI solutions with a focus on AI agents, agentic workflows, and LLM-based applications. The position requires expertise in Python frameworks, React, and AWS AI/ML services like Bedrock and SageMaker. The engineer will collaborate with cross-functional teams to integrate AI into business systems and ensure reliability and scalability. The role is a senior individual contributor position reporting to the AI Tech Lead.

Job Complexity

Easy Hard

AI Insight The role demands 7-8 years of experience, deep knowledge of AI/ML, AWS services, and production-grade deployment, which is challenging but not the hardest.

Salary Analysis

AI Insight The job posting does not specify a salary range. Based on market data for Senior AI Engineers, the median salary is approximately $175,000. This is competitive for a senior role requiring extensive experience and specialized AI skills.

Key Skills

Python AWS Machine Learning LLM AI Agents RAG FastAPI React MLOps GenAI

Cover Letter Sample

Dear Hiring Manager,

I am excited to apply for the Senior AI Engineer position at emerchantpay. With over 8 years of experience in software engineering and AI, I have a strong track record of building production-grade AI solutions using Python, AWS, and modern AI frameworks. My expertise includes designing AI agents, implementing RAG systems, and deploying LLM-based applications at scale.

At my previous role, I led the development of an AI-powered automation platform that reduced operational costs by 30%. I am proficient with AWS Bedrock, SageMaker, and MLOps practices, ensuring robust and scalable deployments.

I am drawn to emerchantpay's innovative approach to payment solutions and would love to contribute to your AI Engineering team. I look forward to the opportunity to discuss how my skills align with your needs.

Sincerely,
[Your Name]

Possible Interview Questions

Describe your experience building AI agents and agentic workflows. How do you ensure they interact safely with internal systems?

I have built AI agents using frameworks like LangChain and AWS Bedrock AgentCore. To ensure safety, I implement strict access controls, validate outputs with guardrails, and use human-in-the-loop for critical actions.

How do you approach designing a RAG system for a large enterprise knowledge base?

I start by understanding the data sources and chunking strategies. I use embeddings from models like text-embedding-ada-002, store vectors in Pinecone or OpenSearch, and implement retrieval with hybrid search and reranking for accuracy.

Can you walk us through your experience with MLOps and deploying ML models to production?

I use tools like MLflow for experiment tracking, Docker for containerization, and AWS SageMaker for deployment. I set up CI/CD pipelines with automated testing and monitoring for model drift and performance.

How do you evaluate the quality and safety of LLM outputs in production?

I implement automated evaluation metrics like BLEU, ROUGE, and custom hallucination detection. I also use human evaluation for edge cases and set up monitoring for harmful content using guardrails.

Describe a challenging AI project you led and how you ensured its success.

I led the development of an AI customer support agent. The challenge was handling diverse intents. I used iterative prompt engineering, few-shot learning, and continuous feedback from logs to improve accuracy, achieving 90% resolution rate.

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.

Apply now >