AI Tech Lead
Indexed 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
- 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
- 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
- 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
emerchantpay is an equal opportunity employer. We appreciate people with different backgrounds and mindsets, and we honor diversity and inclusion.
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