AI Tech Lead

Role Description

We are looking for an AI Tech Lead to lead the design, engineering, and rollout of AI-powered solutions, with a strong focus on AI engineering, AI agents, agentic workflows, applied machine learning, and production-grade AI systems. The role combines hands-on technical leadership, architecture, delivery ownership, and people leadership.

The AI Tech Lead will guide a small but growing AI engineering team and work closely with product, engineering, data, security, infrastructure, and business stakeholders to turn AI opportunities into reliable production systems. The role will have a strong focus on AWS, including advanced AI services such as Amazon Bedrock, Amazon Bedrock AgentCore, Amazon SageMaker, and other AWS AI/ML and cloud-native services.

Responsibilities

• Lead the technical design, architecture, and delivery of AI solutions, with a focus on AI agents, agentic workflows, automation, and AI-assisted business processes.
• Own the end-to-end engineering lifecycle of AI products: discovery, prototyping, evaluation, production implementation, rollout, monitoring, and continuous improvement.
• Lead and manage an AI engineering team, including technical direction, task breakdown, mentoring, code reviews, delivery planning, and engineering quality.
• Design and implement solutions using AWS AI/ML services, including Amazon Bedrock, Amazon Bedrock AgentCore, Amazon SageMaker, and other AWS services for model hosting, orchestration, data processing, monitoring, and security.
• Build and integrate AI applications using technologies such as Python (FastAPI/Flask/Django) or equivalent, along with relevant AI/ML frameworks.
• Design agentic systems that can interact with APIs, internal platforms, business workflows, knowledge bases, and external tools in a safe, observable, and controlled way.
• Define and implement 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, engineering productivity, AI efficiency, automation, and responsible use of AI tools across software delivery.
• 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 model lifecycle management, deployment pipelines, monitoring, evaluation, drift detection, and rollback strategies.
• Collaborate with DevOps, cloud, security, and platform teams to ensure AI systems are production-ready, compliant, cost-efficient, and operationally stable.
• Support rollout and adoption of AI solutions across the organization, including documentation, training, stakeholder communication, and production support.
• Evaluate emerging AI technologies, frameworks, models, and vendors, and provide pragmatic recommendations on adoption.
• Ensure AI solutions follow responsible AI principles, including data privacy, access control, auditability, fairness, explainability where applicable, and secure handling of sensitive data.

Qualifications

• 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, including technical leadership, mentoring, planning, and delivery ownership.
• 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, including practical experience with cloud-native architectures, Amazon Bedrock, Amazon Bedrock AgentCore, Amazon SageMaker, and related AWS AI/ML services (the more, the better).
• Build and integrate AI applications using technologies such as Python (FastAPI/Flask/Django), and relevant AI/ML frameworks.
• Experience with advanced LLM frameworks such as LangChain, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or similar agent/orchestration frameworks.
• Experience building RAG systems, including document ingestion, chunking strategies, embeddings, retrieval evaluation, reranking, and grounding techniques.
• Solid understanding of machine learning concepts, including supervised/unsupervised learning, model training, feature engineering, evaluation, inference, and model performance metrics.
• Experience with MLOps / LLMOps, including CI/CD for ML and AI applications, model deployment, experiment tracking, model/prompt/version management, monitoring, evaluation pipelines, and production rollback strategies.
• Experience with vector databases and retrieval/search technologies, such as Amazon OpenSearch, Pinecone, pgvector, or similar.
• Experience with model fine-tuning, embedding models, transformer architectures, open-source LLMs, and model 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 without creating “AI demo theater” — the system must actually work, scale, and survive contact with real users.
• Strong communication skills, with the ability to explain complex AI and engineering topics to both technical and non-technical audiences.
• Strong ownership mindset, pragmatic decision-making, and ability to balance innovation with delivery discipline.

Considered as an Advantage

• 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 integrating AI systems with enterprise platforms, internal APIs, CRM/ERP systems, ticketing systems, knowledge bases, and workflow engines.
• Experience managing AI adoption programs, internal AI platforms, or organization-wide AI enablement initiatives.
• 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.