Senior AI Engineer

This is a fully remote position, open to applicants in Bulgaria.

📋 Description

• Design, develop, and sustain AI-driven applications, services, and integrations as a member of the AI Engineering team.

• Implement solutions centered around AI agents, agentic workflows, automation, LLM-based applications, and AI-enhanced business processes.

• Create and integrate AI applications utilizing technologies such as Python (FastAPI/Flask/Django) or similar frameworks, React frontends, and applicable AI/ML frameworks.

• Deploy AI solutions using AWS AI/ML services, including Amazon Bedrock, Amazon Bedrock AgentCore, Amazon SageMaker, and additional AWS services for model hosting, inference, orchestration, data processing, monitoring, and security.

• Collaborate closely with the AI Tech Lead to ensure alignment on architecture, technology selections, engineering standards, AI methodologies, and rollout strategies.

• Offer technical advice and guidance to fellow 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 secure and controlled manner.

• Construct and sustain RAG-based solutions, including document ingestion, chunking, embeddings, vector search, retrieval logic, reranking, and grounding techniques.

• Assist in the development and deployment of machine learning models and AI solutions within production environments.

• Contribute to ML pipelines and MLOps practices, encompassing data preparation, model training, experiment tracking, model deployment, monitoring, evaluation, and lifecycle management.

• Integrate LLMs via APIs.

• Establish AI evaluation methods for LLM outputs, RAG quality, agent behavior, model performance, hallucination detection, safety, and reliability.

• Aid in prompt engineering, prompt versioning, function calling, tool utilization, memory patterns, guardrails, and LLM application testing.

• Design and consume APIs while contributing to cloud-based, scalable backend architectures.

• Collaborate with product managers, engineers, data scientists, DevOps, security, and business stakeholders to deliver effective AI solutions.

• Write clean, maintainable, testable, and well-documented code.

• Facilitate production rollouts, troubleshooting, monitoring, optimization, and continuous enhancement of AI systems.

• Stay updated with contemporary AI technologies, frameworks, models, and engineering practices, and provide practical recommendations to the team.

⛳️ Requirements

• Minimum of 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 proven record of deploying machine learning models and AI solutions in production environments.

• Strong hands-on experience in 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.

• In-depth understanding of modern AI concepts, including deep learning, generative AI, LLMs, embeddings, RAG, LLM fine-tuning, and AI evaluation.

• Strong Python development experience, including familiarity with Python (FastAPI/Flask/Django) or similar frameworks.

• Some experience with React for developing user-facing AI tools, internal applications, dashboards, or workflow interfaces.

• Solid knowledge of AWS, including practical experience with cloud-native architectures, Amazon Bedrock, Amazon Bedrock AgentCore, Amazon SageMaker, and other related AWS AI/ML services.

• 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 service providers.

• Experience with ML pipelines and MLOps, including data preparation, model training, model deployment, experiment tracking, model/version management, monitoring, evaluation, and production support.

• Familiarity 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.

• Understanding of API design, microservices, event-driven systems, and cloud-based architectures.

• Good grasp of security and governance requirements for AI systems, including access control, secrets management, data privacy, audit logging, and secure handling of sensitive data.

• Proven experience working in cross-functional teams with engineers, product managers, data scientists, DevOps, security, and business stakeholders.

• Strong problem-solving skills and the ability to convert AI prototypes into reliable, maintainable production systems.

• Excellent communication skills with the ability to articulate technical decisions clearly to both technical and non-technical stakeholders.

🏝️ Benefits

• Fast-growing payment company;

• Excellent working conditions, casual atmosphere, and state-of-the-art hardware;

• Modern, challenging, and continuously evolving business;

• Professional development opportunities – books, training, certifications, etc.;

• Team-building activities and fun events;

• 25 days of paid holiday, plus an extra day for every two years with us;

• Fully distributed and remote working environment.