Senior AI Consultants for the Post-Pilot, Post-EU-AI-Act Enterprise
Hire senior AI consultants with Optiwisers, supplied to enterprises across the UK, France, Switzerland, Germany, Belgium, Netherlands and the Nordics. Nine roles across ML, MLOps, applied AI, LLM engineering, agentic AI, AI architecture, governance and security, mobilised as individual specialists, dedicated teams, managed teams or application management. Boutique precision. Hub-scale capacity.
One AI Consultant Provider. The Full Enterprise AI Stack.
AI sits alongside SAP inside the Optiwisers Technology Talent Hub. Nine AI consultant roles across three layers: foundation ML and data science, applied AI and GenAI, and AI architecture, governance and security. 60+ specialists across the wider bench, 19+ years of delivery, 7+ countries. Cross-stack reinforcement from SAP, Data, Cloud and Cybersecurity where the AI workload needs it.
Precision Match. Not a CV Pipeline.
We don't send CVs. We identify the AI expertise that fits your programme, then integrate it into your team under Optiwisers governance. Boutique precision. Hub-scale capacity. Same senior AI bench whether you need a single MLOps engineer, a full GenAI build squad, an EU AI Act compliance programme or long-term AI platform AMS. Browse by role below, or tell our AI team the outcome you're chasing.
Nearshore Delivery. EU-Compliant. Time-Zone Aligned.
Delivered nearshore from Portugal at 40 to 60% below UK, Swiss and German AI integrator rates. EU-compliant, GDPR and EU AI Act-aligned, same time-zone as your team. Individual AI consultants in one to two weeks. Squads in three to four. Every profile meets our five delivery standards: Validated Profiles, Structured Onboarding, Embedded Governance, Delivery Standards, Reporting & Control. Agility with structure. We move fast. But never blindly. Response within one business day.
Every AI Consultant Role on Our Bench
Nine AI consultant roles grouped by three buyer contexts: the foundation ML and data science layer, the applied AI and GenAI layer, and the architecture, governance and security layer. Each role is delivered nearshore from Portugal as an individual specialist, a dedicated team, a managed team or an application management engagement.
Four Ways to Engage the AI Bench
Same senior AI bench, four commercial structures. Each model was built for a different phase of the AI programme lifecycle.
Frequently Asked Questions
Common questions about hiring Optiwisers AI consultants.
Optiwisers covers nine AI consultant roles across three buyer contexts. Foundation ML and data science: ML engineers, MLOps engineers, Data scientists. Applied AI and GenAI: AI engineers (RAG systems, applied AI), LLM/GenAI engineers (fine-tuning, deep LLM work), Hyperautomation & agentic AI consultants. AI architecture, governance and security: AI solutions architects, AI governance consultants (EU AI Act), AI security engineers. Data engineering roles that feed AI systems live under our Data & Analytics hub. Cloud infrastructure for AI workloads lives under our Cloud hub.
Yes. AI governance consultants are one of the nine roles on our bench, and EU AI Act work is their primary focus in 2026. High-risk AI system requirements under Annex III became fully enforceable on 2 August 2026, with fines reaching EUR 35 million or 7% of global revenue. Typical scope includes AI system inventory and classification against the four-tier risk framework, Article 11 technical documentation, quality management systems, Fundamental Rights Impact Assessments (FRIAs) where applicable, human oversight mechanisms, post-market monitoring and EU database registration. A senior AI governance consultant on Time & Materials typically starts within one to two weeks. Full compliance programmes for mid-to-large enterprises take 8 to 14 weeks depending on AI system inventory scope.
Yes, this is a common intervention. Only 25% of enterprise AI initiatives have delivered expected ROI per IBM research, and Gartner expects 40% of agentic AI projects to be cancelled by 2027, with quality and governance gaps as the main failure modes. Typical intervention patterns include a RAG system where retrieval quality broke at production scale, an MLOps pipeline with unmanaged model drift, a fine-tuned LLM with unresolved evaluation gaps, or an agentic workflow that never left the demo environment. A senior AI architect enters mid-flight, runs a review focused on the real bottleneck (data, evaluation, infrastructure, governance or a mix), and reshapes the programme to reach production. Mobilisation in one to two weeks.
We deliver nearshore to enterprises across the UK, France, Switzerland, Germany, Belgium, Netherlands and the Nordics. Delivery originates from our EU-based hub in Portugal, giving clients time-zone alignment across all these markets and EU-compliant delivery under GDPR and the EU AI Act (relevant for high-risk AI systems in banking, healthcare, insurance, employment and public sector). We also serve Iberian clients in Portugal and Spain, and support US clients on AI application management engagements where remote delivery models fit.
Time to mobilise depends on the engagement model. Individual Specialists on Time & Materials typically start within one to two weeks of scope confirmation. Dedicated Teams stand up within three to four weeks. Managed Teams and Application Management engagements are structured over four to six weeks. Niche AI roles (specific model certifications, industry-regulated use cases, senior LLM research profiles) require an additional two to three weeks for sourcing and validation. We work from a pre-validated bench across the nine AI roles. And we respond to every enquiry within one business day.
AI engineers own the applied AI application layer: RAG systems, prompt engineering, LLM integration, agent orchestration and vector databases. Their work sits on top of foundation models rather than training them. ML engineers own the model training and evaluation loop: classical ML with scikit-learn or PyTorch, feature engineering, model selection and evaluation frameworks. Most modern AI programmes need both. RAG and agentic AI systems built by AI engineers still rely on classical ML for reranking, embedding evaluation and downstream classification tasks handled by ML engineers.
Yes. RAG systems are the primary focus of the AI engineers role, covering vector database selection (Pinecone, Weaviate, pgvector), retrieval strategy, prompt engineering, evaluation frameworks and production integration. Agentic AI and hyperautomation sit with the hyperautomation & agentic AI consultants role, covering multi-agent orchestration patterns, task-specific autonomous systems and integration with orchestration platforms like Salesforce Agentforce and Microsoft Copilot Studio. LLM fine-tuning, model evaluation, distillation and embedding strategy sit with the LLM/GenAI engineers role. Multi-discipline engagements typically combine one AI engineer, one LLM engineer and an AI solutions architect on a single squad.
Yes. AI security engineers are one of the nine roles on our bench, and their scope is distinct from general cybersecurity. Typical work covers prompt injection defence, output filtering, RAG data leakage prevention, adversarial robustness testing, model red-teaming and integration with the OWASP LLM Top 10 threat framework. Most enterprise LLM deployments combine an AI security engineer with a cloud security engineer from the Cybersecurity hub for a full attack-surface assessment. AI security is where a lot of otherwise-approved LLM systems get blocked at the last mile of enterprise deployment.
Let's Start the AI Conversation
Whether the outcome is an EU AI Act compliance programme, a RAG system that survives production traffic, an MLOps stand-up, an agentic workflow rollout, or a single senior AI architect to reinforce a workstream, our AI bench is ready. Tell us your challenge and we respond within one business day.