{"id":760807,"url":"https://alion.io/job/emerchantpay-ai-tech-lead","title":"AI Tech Lead","company":{"id":172196,"name":"emerchantpay","domain":"emerchantpay.com","url":"https://alion.io/company/emerchantpay","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"D","score":53,"open_postings":14,"ghost_share":0.786,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-31T05:58:55Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Sofia, Bulgaria"],"countries":["BG"],"hiring_countries":["BG"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":49000,"max_usd":122000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1286},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Amazon ECS","optional":false},{"name":"Amazon EKS","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"AutoGen","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"AWS Bedrock AgentCore","optional":false},{"name":"AWS CDK","optional":false},{"name":"CI/CD","optional":false},{"name":"CloudFormation","optional":false},{"name":"CrewAI","optional":false},{"name":"Django","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"ETL/ELT","optional":false},{"name":"FastAPI","optional":false},{"name":"Feature Store","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Flask","optional":false},{"name":"Function Calling","optional":false},{"name":"Hallucination","optional":false},{"name":"LangChain","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"Machine Learning","optional":false},{"name":"OpenSearch","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"Terraform","optional":false},{"name":"Kubernetes","optional":true},{"name":"PostgreSQL","optional":true}],"status":"live","first_seen_at":"2026-05-13T00:00:00Z","employer_posted_date":"2026-05-13","last_verified_at":"2026-10-31T12:45:50Z","board_verified":true,"closed_at":null,"days_open":171,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":171},"description":"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.\nWe 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.\nThe 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.\nThe 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.\nResponsibilities\nLead the technical design, architecture, and delivery of AI solutions, with a focus on AI agents, agentic workflows, automation, and AI-assisted business processes.\nOwn the end-to-end engineering lifecycle of AI products: discovery, prototyping, evaluation, production implementation, rollout, monitoring, and continuous improvement.\nLead and manage an AI engineering team, including technical direction, task breakdown, mentoring, code reviews, delivery planning, and engineering quality.\nDesign 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.\nBuild and integrate AI applications using technologies such as Python (FastAPI/Flask/Django) or equivalent, along with relevant AI/ML frameworks.\nDesign agentic systems that can interact with APIs, internal platforms, business workflows, knowledge bases, and external tools in a safe, observable, and controlled way.\nDefine and implement best practices for LLM application development, including prompt engineering, RAG, tool use, function calling, memory, evaluation, guardrails, and hallucination mitigation.\nDrive improvements in internal engineering practices around AI-assisted development, engineering productivity, AI efficiency, automation, and responsible use of AI tools across software delivery.\nWork with stakeholders to identify high-value AI use cases, assess feasibility, define success metrics, and prioritize delivery.\nEstablish engineering standards for AI systems, including code quality, testing, observability, reliability, security, scalability, and maintainability.\nDrive MLOps and LLMOps practices, including model lifecycle management, deployment pipelines, monitoring, evaluation, drift detection, and rollback strategies.\nCollaborate with DevOps, cloud, security, and platform teams to ensure AI systems are production-ready, compliant, cost-efficient, and operationally stable.\nSupport rollout and adoption of AI solutions across the organization, including documentation, training, stakeholder communication, and production support.\nEvaluate emerging AI technologies, frameworks, models, and vendors, and provide pragmatic recommendations on adoption.\nEnsure AI solutions follow responsible AI principles, including data privacy, access control, auditability, fairness, explainability where applicable, and secure handling of sensitive data.\nRequirements\nMinimum 10 years of professional experience in software engineering, data engineering, machine learning engineering, AI engineering, or related technical roles.\nAt least 3 years of experience leading or managing engineering teams, including technical leadership, mentoring, planning, and delivery ownership.\nStrong hands-on experience building production-grade AI, ML, and data-driven systems.\nPractical experience with AI agents, agentic workflows, LLM-based applications, workflow automation, tool-calling architectures, and AI orchestration patterns.\nStrong 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).\nBuild and integrate AI applications using technologies such as Python (FastAPI/Flask/Django), and relevant AI/ML frameworks.\nExperience with advanced LLM frameworks such as LangChain, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or similar agent/orchestration frameworks.\nExperience building RAG systems, including document ingestion, chunking strategies, embeddings, retrieval evaluation, reranking, and grounding techniques.\nSolid understanding of machine learning concepts, including supervised/unsupervised learning, model training, feature engineering, evaluation, inference, and model performance metrics.\nExperience 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.\nExperience with vector databases and retrieval/search technologies, such as Amazon OpenSearch, Pinecone, pgvector, or similar.\nExperience with model fine-tuning, embedding models, transformer architectures, open-source LLMs, and model benchmarking.\nExperience designing APIs, microservices, event-driven systems, and cloud-native backend architectures.\nStrong understanding of security and governance requirements for AI systems, including access control, secrets management, data privacy, audit logging, and safe use of sensitive data.\nExperience working with cross-functional teams, including product managers, architects, engineers, data scientists, security teams, and business stakeholders.\nAbility to move from prototype to production without creating “AI demo theater” - the system must actually work, scale, and survive contact with real users.\nStrong communication skills, with the ability to explain complex AI and engineering topics to both technical and non-technical audiences.\nStrong ownership mindset, pragmatic decision-making, and ability to balance innovation with delivery discipline.\nConsidered as an Advantage\nExperience with containerization and orchestration, including Docker and EKS/ECS.\nExperience with infrastructure as code using Terraform, AWS CDK, or CloudFormation.\nExperience with data platforms, ETL/ELT pipelines, data lakes, feature stores, and real-time data processing.\nExperience implementing responsible AI controls, AI governance frameworks, safety guardrails, and compliance processes.\nExperience integrating AI systems with enterprise platforms, internal APIs, CRM/ERP systems, ticketing systems, knowledge bases, and workflow engines.\nExperience managing AI adoption programs, internal AI platforms, or organization-wide AI enablement initiatives.\nContributions to open-source AI/ML projects, published technical content, conference talks, or patents in AI/ML-related areas.\nAWS certifications, especially in architecture, machine learning, security, or DevOps.\nExperience in fintech.\nBenefits\nFast-growing payment company;\nExcellent working conditions, casual atmosphere, and state-of-the-art hardware;\nModern, challenging, constantly growing business;\nProfessional development - books, trainings, certifications, etc.;\nTeam buildings and fun activities;\n25 days paid holiday, 1 day for every 2 years with us;\nFully distributed and remote.\nIf you are interested, please apply with your CV in English only. Only short-listed candidates will be contacted.\nPersonal data of the applicants will be processed in strict confidentiality by emerchantpay ltd. UIC 175117520 solely for the purposes of selection and recruitment and will not be transferred to other data controllers unless required by law. Applicants provide their personal data on a voluntary basis and will have the right to access and correct their personal data within a reasonable time upon filing a written request.\nemerchantpay is an equal opportunity employer. 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