Senior AI Engineer

emerchantpay

Senior AI Engineer at emerchantpay designing and implementing AI solutions with a focus on modern AI technologies and AWS services. Collaborating with cross-functional teams for reliable AI capabilities.

Posted 5/18/2026full-timeRemote • 🇧🇬 BulgariaSeniorWebsite

Tech Stack

Tools & technologies

AWSCloudDjangoFlaskMicroservicesPythonPyTorchReactTensorflow

About the role

Key responsibilities & impact

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

What you’ll need

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.

Benefits

Comp & perks

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.

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Hard Skills & Tools

PythonFastAPIFlaskDjangoReactAWSAmazon BedrockAmazon SageMakerPyTorchTensorFlow

Soft Skills

problem-solvingcommunicationcollaborationtechnical guidancecode qualitytestingobservabilityproduction readinesscontinuous improvementstakeholder engagement