Career / AI Executive & Architect · Engagement 2 of 2
EPAM Systems
Principal Director — Data Analytics & Generative AI
Aug 2024 – Jun 2025 · Zurich · Employment concluded following an internal restructuring.
35Generative AI use cases mapped across all seven divisions of a Tier-1 Swiss bank
Skills usedAI governance, risk & regulatory complianceMachine learning, deep learning & MLOpsData Strategy for AIGenerative AI & LLM solutionsAgentic AI & AI-native engineeringSolution & enterprise architectureData architecture, engineering & governanceSemantic engineering & knowledge managementCloud & multi-tenant SaaS platformsInnovation & change managementTeam leadership & talent developmentBusiness value & financial managementProgramme, project & vendor managementExecutive advisory & pre-salesDigital marketing & customer analyticsCommunication & thought leadership
IndustriesRetail, consumer goods & e-commerceScientific research & academiaPublic sector & international organisationsBiopharma, global health & life sciencesAgriculture, food & environmentTechnology consulting & IT servicesLogistics & supply chainManufacturing, chemicals & industrialsBanking & financial servicesEnergyTelecommunications
All 29 contributions
Practice leadership
- Spearheaded the global expansion of EPAM’s Data & AI practice across EMEA, North America, APAC, LATAM and CIS, defining strategic direction for AI, Generative AI and Agentic AI, and led client engagements and pre-sales across banking, government services, logistics, consumer goods, biopharma, chemicals, telecommunications, energy and industrials.
Client: a Tier-1 Swiss bank (proposal work, no implementation)
- Led the technical response to a Tier-1 Swiss bank’s enterprise AI RFI — structured on business value; technology, security and compliance; and people, operating model and governance — later reused as the technical core of a second pursuit with the same bank and the engagement behind EPAM’s preferred-vendor status.
- Designed the technical questionnaire responses on model orchestration, multi-LLM integration, RAG, embedding strategies and secure deployment, articulating EPAM’s LLMOps approach, AI Factory and compliance measures, and coordinated contributions from global EPAM teams.
- Coordinated the proposal for a bank-wide Generative AI blueprint mapping 35 prioritised use cases across all seven divisions of the bank over six enabling layers: AI strategy, legal and compliance, data and security, technical architecture, responsible AI and workforce enablement.
- Extended the proposition to generative AI in prudential and regulatory reporting (proposal scope, not delivered).
- Adapted EPAM’s Generative AI Factory target architecture for the bank — three planes (Models / Data / Services) over Data Factory, AI Backoffice and AI Serving Platform — covering feature and prompt engineering, continuous training and evaluation, data- and model-drift monitoring, hallucination management, guardrails, vector stores, FinOps and a gateway to third-party models, closing on a dedicated data-privacy layer.
- Contributed the AI and data proposition to a growth proposal for the bank’s China digital wealth platform (200,000+ users in two years, in a market where over 80% of wealth management is domestically held): a gamified paper-trading experience with a graduated three-tier conversion model, disclosure design and an explicit Chinese gaming-regulation risk assessment.
Client pursuits
- Major US county government: as principal AI/GenAI expert, spearheaded the response to an RFP for enterprise-wide AI and automation services — designed the global delivery model, defined the AI Factory approach and translated requirements into modular solutions for GenAI, automated document processing and operational decision support.
- Global pharmaceutical company: principal expert (GenAI, architecture, software engineering) supporting the AI Factory framework and global AI strategy; defined a reusable AI component framework and LLM/multi-agent orchestration for biomedical R&D and regulatory processes, and a global Community of Practice for AI knowledge sharing.
- Directed engagements in data engineering and cloud platforms, defining enterprise data-platform strategies on Snowflake, BigQuery and Synapse and advising on data contracts, service tiering and pipeline monitoring.
- Global energy leader, Saudi Arabia: designed scalable AWS AI cloud platforms and end-to-end architectures across upstream, downstream, R&D, engineering, finance, HR, safety and supply chain, with LLM orchestration, multi-agent systems, RAG and LSTM models for R&D.
- Leading telecom operator, Africa: co-led the RFP response on enterprise AI/GenAI strategy, developing a target-state federated AI architecture, AI Factory delivery capabilities and a GenAI integration roadmap.
- Global chemical manufacturer: lead architect for GenAI in R&D, engineering and production — semantic ingestion pipelines, RAG over structured and unstructured data, and a phased path from PoCs to multi-agent assistants on a modular, scalable architecture.
- Global industrial materials firm, UK: defined the architecture for freight anomaly detection — hybrid statistical, ML and GenAI models with RAG for contextual validation on AWS and GCP, feature engineering, prompt design for anomaly justification and EPAM AI accelerators.
- Global agribusiness and food company, Switzerland: defined the GenAI strategy for shipping and transport optimisation, structuring 18+ use cases (laytime calculations, contract analysis, chartering risk mitigation) with OCR, NLP, LLMs and knowledge graphs.
- Leading logistics and postal group, UK: designed a strategic MLOps assessment offering for enterprise-wide AI/ML adoption, with an evaluation methodology across data ops, model management and platform readiness.
- Global coffee company, Netherlands: led the analytical assessment of predictive analytics for global shipment delays — historical shipping and customs data, key delay drivers, model performance — and delivered the resulting AI strategy and offering.
AI governance, risk & operations
- Led an AI regulatory readiness assessment aligned with the EU AI Act, identifying system classifications, governance gaps, documentation requirements, human-oversight controls, transparency obligations and post-deployment monitoring needs.
- Established model-risk controls for predictive and generative AI, including independent validation, bias testing, explainability review, performance thresholds, approval gates, audit trails and periodic revalidation.
- Led privacy-by-design reviews for AI systems handling sensitive or personal data, covering data minimisation, retention, data residency, DPIA inputs, access governance, encryption and secure data sharing.
- Defined security controls for LLM and AI solutions: identity and access management, secrets management, encryption, network segmentation, prompt-injection defences, content filtering, audit logging, vulnerability management and secure SDLC.
- Prepared AI, data and analytics platforms for internal audit, client assurance, regulatory review and quality inspections with evidence repositories, control matrices, validation records and governance documentation.
- Established a production MLOps operating model: experiment tracking, feature management, model registry, reproducible pipelines, CI/CD, automated testing, deployment controls, rollback, monitoring and scheduled retraining.
- Defined an LLMOps evaluation framework for RAG and agentic systems measuring retrieval relevance, groundedness, factuality, hallucination rate, task completion, latency, cost per interaction, safety and user satisfaction.
- Set service-level objectives for production AI services: availability, latency, extraction accuracy, retrieval quality, evaluation scores, deficiency handling, incident response and recovery time.
- Implemented end-to-end observability across data pipelines, retrieval layers, LLM calls, agent workflows, model outputs, user feedback, infrastructure performance and operational cost.
- Introduced AI FinOps to monitor and optimise cloud spend, GPU consumption, token usage, model routing, embedding costs, vector-store capacity and cost-to-serve by product or client.
- Defined AI incident-management procedures for model degradation, data-quality failures, hallucination events, security incidents, service interruptions and regulatory escalation, with post-incident review and corrective-action tracking.
- Implemented formal model-lifecycle governance: versioning, approval, retirement, evaluation refresh, data-drift detection and policy updates for predictive models and LLM applications.