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AI Tech Lead

3 months, 3 weeks ago
Bulgaria
Full-time
Lead
AI Engineer
Software Development
AWS AWS CDK CloudFormation CRM Django Docker ERP FastAPI Flask Machine Learning Microservices MLOps Python Terraform
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emerchantpay

emerchantpay

emerchantpay specializes in providing seamless and secure online, mobile, and in-store payment processing solutions, along with risk and fraud management services, to help merchants enhance their conversion rates and expand their customer reach globally.

Diversified Financial Services
251-1K
Founded 2002
View All Jobs 11

Description

  • Lead the technical design, architecture, and delivery of AI solutions focused on AI agents, agentic workflows, automation, and AI-assisted business processes.
  • Own the end-to-end engineering lifecycle of AI products from discovery and prototyping through evaluation, production implementation, rollout, monitoring, and continuous improvement.
  • Lead and manage a small but growing AI engineering team through technical direction, task breakdown, mentoring, code reviews, planning, and quality oversight.
  • Design and implement solutions using AWS AI/ML services such as Amazon Bedrock, Amazon Bedrock AgentCore, and Amazon SageMaker.
  • Build and integrate AI applications using Python-based web frameworks and relevant AI/ML frameworks.
  • Design safe, observable, and controlled agentic systems that interact with APIs, internal platforms, knowledge bases, and external tools.
  • Define best practices for LLM application development, including prompt engineering, RAG, tool use, function calling, memory, evaluation, guardrails, and hallucination mitigation.
  • Drive improvements in internal engineering practices around AI-assisted development, productivity, automation, and responsible AI tool use.
  • Work with stakeholders to identify high-value AI use cases, assess feasibility, define success metrics, and prioritize delivery.
  • Establish engineering standards for AI systems, including code quality, testing, observability, reliability, security, scalability, and maintainability.
  • Drive MLOps and LLMOps practices, including deployment pipelines, monitoring, evaluation, drift detection, and rollback strategies.
  • Collaborate with DevOps, cloud, security, and platform teams to ensure systems are production-ready, compliant, cost-efficient, and operationally stable.
  • Support rollout and adoption of AI solutions through documentation, training, stakeholder communication, and production support.
  • Evaluate emerging AI technologies, frameworks, models, and vendors and recommend practical adoption choices.
  • Ensure AI solutions follow responsible AI principles, including privacy, access control, auditability, fairness, explainability, and secure handling of sensitive data.

Requirements

  • Minimum 10 years of professional experience in software engineering, data engineering, machine learning engineering, AI engineering, or related technical roles.
  • At least 3 years of experience leading or managing engineering teams with responsibility for technical leadership, mentoring, planning, and delivery.
  • Strong hands-on experience building production-grade AI, ML, and data-driven systems.
  • Practical experience with AI agents, agentic workflows, LLM-based applications, workflow automation, tool-calling architectures, and AI orchestration patterns.
  • Strong knowledge of AWS cloud-native architectures and AI/ML services, including Amazon Bedrock, Amazon Bedrock AgentCore, and Amazon SageMaker.
  • Experience building AI applications with Python and frameworks such as FastAPI, Flask, or Django.
  • Experience with advanced LLM frameworks such as LangChain, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or similar.
  • Experience building RAG systems, including document ingestion, chunking, embeddings, retrieval evaluation, reranking, and grounding techniques.
  • Solid understanding of machine learning concepts, including training, feature engineering, evaluation, inference, and performance metrics.
  • Experience with MLOps / LLMOps, including CI/CD, deployment, experiment tracking, version management, monitoring, evaluation pipelines, and rollback strategies.
  • Experience with vector databases and retrieval/search technologies such as Amazon OpenSearch, Pinecone, or pgvector.
  • Experience with model fine-tuning, embedding models, transformer architectures, open-source LLMs, and benchmarking.
  • Experience designing APIs, microservices, event-driven systems, and cloud-native backend architectures.
  • Strong understanding of security and governance requirements for AI systems, including access control, secrets management, data privacy, audit logging, and safe use of sensitive data.
  • Experience working with cross-functional teams, including product managers, architects, engineers, data scientists, security teams, and business stakeholders.
  • Ability to move from prototype to production and deliver systems that work reliably at scale.
  • Strong communication skills for explaining complex AI and engineering topics to technical and non-technical audiences.
  • Strong ownership mindset, pragmatic decision-making, and ability to balance innovation with delivery discipline.
  • Experience with containerization and orchestration, including Docker and EKS/ECS, is an advantage.
  • Experience with infrastructure as code using Terraform, AWS CDK, or CloudFormation is an advantage.
  • Experience with data platforms, ETL/ELT pipelines, data lakes, feature stores, and real-time data processing is an advantage.
  • Experience implementing responsible AI controls, governance frameworks, safety guardrails, and compliance processes is an advantage.
  • Experience integrating AI systems with enterprise platforms, internal APIs, CRM/ERP systems, ticketing systems, knowledge bases, and workflow engines is an advantage.
  • Experience managing AI adoption programs, internal AI platforms, or organization-wide AI enablement initiatives is an advantage.
  • Contributions to open-source AI/ML projects, published technical content, conference talks, or patents in AI/ML-related areas are an advantage.
  • AWS certifications in architecture, machine learning, security, or DevOps are an advantage.
  • Experience in fintech is an advantage.

Benefits

  • Fast-growing payment company.
  • Excellent working conditions with a casual atmosphere and state-of-the-art hardware.
  • A modern, challenging, and constantly growing business.
  • Professional development support, including books, trainings, and certifications.
  • Team buildings and fun activities.
  • 25 days of paid holiday, plus 1 extra day for every 2 years with the company.
  • Fully distributed and remote work.

Interested in this position?

Apply directly on the company website

Apply Now

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