AI Tech Lead

emerchantpay

1d•Remote

About The Position

emerchantpay is a leading global payment service provider and acquirer for online, mobile, in-store and over the phone payments. Our global payments solution is available through a simple integration, offering a diverse range of features, including global acquiring, global and local payment methods, advanced fraud management and performance optimisation. We empower businesses to design seamless and engaging payment experiences for their consumers. 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.

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, 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.

Nice To Haves

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.

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.