Agentic AI & AI-native engineering

Multi-agent systems, governed agentic delivery lifecycles, AI-generated code, MCP tooling.

29 contributions across 4 experiences

Where this skill was applied

Often together withGenerative AI & LLM solutions Solution & enterprise architecture AI governance, risk & regulatory compliance Cloud & multi-tenant SaaS platforms Data Strategy for AI Business value & financial management

2025–20267 contributions

Solara28

Founded, ideated, architected and single-handedly developed Solara28, an AI-native multi-tenant SaaS platform for European commerce verticals — food services, restaurants, pharmacies, grocery retailers, wine and spirits, catering, coffee shops and event ticketing — positioned under the vision “Illuminate your commerce” as a local-first, zero-commission alternative to global marketplace aggregators and platforms such as Amazon, Shopify, Just Eat, Deliveroo, PrestaShop and WooCommerce. Evidence ↗
Architected and implemented a governed Agentic Factory coordinating eight specialised virtual teams and 32 autonomous AI agents across the end-to-end software delivery lifecycle, generating 6,000,000+ lines of production-ready code, documentation and change artefacts in under one year across a polyglot stack (TypeScript, Supabase/PostgreSQL, FastAPI, Stripe, Vercel).
Established Solara28 as a large-scale reference implementation of Agentic AI product engineering, demonstrating the viability of autonomous multi-agent systems in enterprise-grade software delivery.
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.
Designed as next-generation commerce infrastructure for local economies, combining AI-native operations, multi-tenant SaaS architecture and decentralised business ownership to support European business sovereignty and restore data sovereignty, margin control and customer ownership to independent merchants.
Established an end-to-end autonomous AI delivery model covering architecture design, code generation, testing, security validation, cloud implementation and documentation, with every artefact traceable to requirements, architecture decisions, source-control changes, code review, automated testing, security scanning and release approval; the Agentic Factory executed work across engineering, QA, DevOps, compliance, localisation, marketing, branding, e-commerce and online payments.
Governed the automated delivery lifecycle — architecture, software engineering, testing, security, DevOps and documentation — as an agentic AI operating model aligned with the EU AI Act.

2024–20256 contributions

EPAM Systems

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.
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.
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.
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.

20264 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.
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.

2002–20111 contribution

United Nations CGIAR

Developed a digital crop-identification platform combining taxonomies, scientific datasets and AI-assisted recognition for agricultural biodiversity research.

Profile8 contributions

Across the career

Uses Solara28 as a concrete reference case for AI-native product strategy, operational efficiency, platform architecture and business-model innovation.
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.
Demonstrates modern delivery leadership by orchestrating specialised AI-agent teams across architecture, implementation, documentation, testing and governance.
Designs agentic development methodologies in which autonomous AI agents support architecture, code generation, testing, security review, documentation and delivery governance, along with hands-on agentic AI architectures and platforms.
Applies continuous code generation, architecture review, security scanning, test-driven development and human oversight to AI-native engineering.
Demonstrates how AI-agent orchestration can extend the delivery capacity of a traditional cross-functional product organisation.
Advises C-suite and senior leadership on data governance, AI, agentic AI operating models and workforce transformation, and mentors executive teams moving towards data- and AI-led organisations.

Skills3 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.
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.
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