Generative AI & LLM solutions

LLM applications, retrieval-augmented generation, multi-provider LLM layers, copilots and conversational AI.

43 contributions across 4 experiences

Where this skill was applied

Often together withAI governance, risk & regulatory compliance Machine learning, deep learning & MLOps Agentic AI & AI-native engineering Solution & enterprise architecture Data Strategy for AI Cloud & multi-tenant SaaS platforms

2024–202517 contributions

EPAM Systems

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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Implemented end-to-end observability across data pipelines, retrieval layers, LLM calls, agent workflows, model outputs, user feedback, infrastructure performance and operational cost.
Implemented formal model-lifecycle governance: versioning, approval, retirement, evaluation refresh, data-drift detection and policy updates for predictive models and LLM applications.

20267 contributions

Gavi, the Vaccine Alliance

Established and operates the Innovation Lab’s experimental AI tooling estate on Microsoft Frontiers models in Azure — including Cowork, Copilot Claude, MS Scout and OpenClaw for agentic-AI experimentation and architecture evaluation, alongside Cursor, Lovable, Bolt, v0, Microsoft 365 Copilot, Copilot Studio and Azure AI Foundry — under defined tool-selection rules, cost guardrails and an approved/excluded tooling list.
Leads AI enablement through a three-tier training architecture, a curated prompt library, an intranet AI Self-Service Hub and an organisation-wide AI Community of Practice with departmental champions — targeting 80%+ awareness-training completion and 70%+ monthly active Copilot users in year one.
Designed the Azure architecture extending Gavi’s Programmatic Dashboard from reactive reporting to predictive early warning, forecasting country × vaccine stock-buffer breaches 6–12 months ahead — integrating SAP, UNICEF SD/Thrive360 and VaxNeed into Azure SQL and a row-level-secured Power BI model, with forecasting on Azure Machine Learning and grounded narrative generation on Azure OpenAI behind an API Management LLM gateway.
Skills and technologies: Azure solution and security architecture (App Service, Static Web Apps, Functions, SQL, Blob, Key Vault, API Management, Data Factory, Machine Learning, AI Foundry, AI Search, Monitor) · Snowflake · Azure OpenAI, RAG, multi-agent and agentic AI · MLOps/LLMOps and AI evaluation · AI governance, Responsible AI and EU-aligned AI risk management · Entra ID, RLS, TDE/CMK, PIM, managed identities, TLS · C4 and TOGAF · Power BI, Power Platform, Copilot Studio, Purview, Sentinel · confidential-data governance · innovation portfolio governance, vendor selection and procurement · Design Thinking and rapid prototyping facilitation.
Operationalised AI governance controls covering data leakage, agent proliferation, cost overruns, prompt injection, hallucination and shadow AI, with per-agent monthly credit limits and threshold alerting, DLP connector policies blocking third-party connectors, and monitoring across Power Platform Admin Center, Microsoft Purview and Microsoft Sentinel, reported monthly to the AI Steering Committee.
Established the reusable reference architecture for grounding Copilot agents in enterprise knowledge — SharePoint → Azure Blob → Snowflake → Azure AI Search vector index → Power Automate → Azure OpenAI → Copilot in Teams/SharePoint — secured with Entra ID RBAC, Key Vault and Purview DLP, with mandatory citation traceability and token-consumption cost controls.
Defined a multi-tier Governance Copilot ecosystem separating the user-facing Governance Bot, a Governance Knowledge Hub and a Power Automate + Azure OpenAI Query Executor, with Azure Blob ingestion, Azure AI Search vector indexing, Entra ID single sign-on and Azure Monitor / Log Analytics monitoring of latency, token usage and errors.

2025–20264 contributions

Solara28

Produced promotional videos with AI video generation (Seedance 2.5 in Dreamina).
Designed, built and published the open-source Buonaiuto-Doc4LLM MCP server, which auto-detects a project’s library stack, fetches current official documentation and serves it version-aware to AI coding assistants — the RAG infrastructure that kept the agents’ 6M+ lines of code aligned with current, secure architectural standards and eliminated API hallucinations.
Architected a provider-agnostic LLM service layer (TypeScript/Node.js/Supabase) connecting OpenAI, Anthropic and Gemini APIs, with configurable provider settings, prompt governance, conversation logging and health monitoring, powering conversational and voice-enabled commerce (“Talk to Your Shop”), automated catalogue ingestion from PDFs, photos and Excel, and predictive revenue forecasting.
Embedded operational AI directly into the commerce execution layer: one-click PDF-to-live-store catalogue imports, natural-language conversational assistants, voice-enabled customer ordering via chatbots, a Merchant AI Assistant and predictive CRM analytics built natively into the platform.

2019–20242 contributions

DSM-Firmenich

Pioneered an AI hybrid digital twin predicting unintended manufacturing-plant shutdowns that reduced downtime by 48% and earned the Best DSM Corporate Research Project award, using deep learning (LSTM, self-attention) on 768 real-time sensor streams and LLMs (GPT-3.5, Llama 2) in LangChain for maintenance-log interpretation.
Led an information-retrieval project governing millions of lab documents with NLP and transformers (BERT, GPT) for keyword extraction and synonym generation, plus an ontology integrated with public data sources to improve research data interoperability.

Profile8 contributions

Across the career

AI, Data and Technology Executive with 15+ years of international experience in artificial intelligence, Generative AI, machine learning, data strategy and enterprise transformation, combining executive-level AI strategy with hands-on technical leadership and principal engineering experience in AI and Generative AI.
Builds and directs hybrid teams of people and AI agents, including swarms of AI agents, drawing on 30,000+ prompts of hands-on generative AI work.
Specialises in Generative AI, LLMs, RAG, Agentic AI, predictive analytics, machine learning, MLOps, LLMOps, AI governance, AI factories and model evaluation; designs enterprise AI and data strategies and defines AI technology stacks and long-term machine-learning roadmaps through hands-on technical leadership.
Translates emerging AI capabilities into practical business operations, including document ingestion, segmentation, forecasting, optimisation, personalisation and conversational assistance.
Leads enterprise AI literacy and adoption through training, prompt libraries, self-service resources, communities of practice and departmental champions at Gavi and EPAM.
Converts unstructured scientific or commercial information — menus, catalogues, images, PDFs — into structured operational data using computer vision, LLMs, extraction pipelines and governed data models.
Applies Generative AI to merchant operations, customer engagement, catalogue management, forecasting, personalisation, route optimisation and fraud detection.
Designs natural-language business assistants and conversational customer experiences using multi-LLM orchestration.

Skills5 contributions

Skills & applied knowledge

AI and Generative-AI technologies: GANs, LLMs, agentic AI, FinOps, Trustworthy AI, MLOps, LLMOps, AI Factory strategy, MCP, A2A, multi-agent systems and Azure AI Foundry.
Enterprise productivity, automation and security: Microsoft 365, Project, Visio, Power Automate, Copilot, Entra, Azure Key Vault, Defender for Cloud, Sentinel, Azure Policy and Purview.
Has used AI development tools since 2014, with experience in complex AI-assisted and agentic software development: Cursor, Claude Code, ChatGPT Codex, Kimi, DeepSeek, Z GLM, n8n, Obsidian, LangGraph, Midjourney, Seedance 2.5, PyTorch, Google Gemini and multi-agent workflows.
Five years of hands-on experience with Large Language Models and LLM-based applications.
Model Context Protocol (MCP) and Generative Engine Optimization (GEO): making products and content usable and discoverable by AI assistants and agents.

All skills

Artificial intelligence

AI governance, risk & regulatory compliance 81 Machine learning, deep learning & MLOps 53 Data Strategy for AI 44 Generative AI & LLM solutions 43 Agentic AI & AI-native engineering 29

Architecture & data platforms

Solution & enterprise architecture 62 Data architecture, engineering & governance 54 Semantic engineering & knowledge management 50 Cloud & multi-tenant SaaS platforms 41

Leadership & business

Innovation & change management 42 Team leadership & talent development 31 Business value & financial management 29 Programme, project & vendor management 27 Executive advisory & pre-sales 19 Entrepreneurship & venture building 13

Product, marketing & communication

Digital marketing & customer analytics 27 Product & full-stack engineering 23 Communication & thought leadership 10