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AI Tech Lead

4 weeks, 1 day 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
Apply Now
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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