Hire AI Engineers
Senior AI engineers with production experience in LLMs, machine learning, NLP and computer vision. Vetted through real delivery, integrated into your team in about two weeks. Results in production, not experiments.
Where do you hire AI engineers who deliver in production?
Softblues is a London-registered AI development company and a Registered Anthropic Partner Network member, also a Google Cloud Partner and a registered Microsoft partner. We add senior AI engineers to your team from a bench that is vetted through real delivery, people who have shipped LLM, agent, machine-learning and computer-vision systems to production and kept them running. You tell us the role and the outcome on a scoping call, we shortlist from the bench, you interview and choose, and the engineer integrates in about two weeks.
Why dedicated AI expertise?
Anyone can call an API and get a demo working. Far fewer can take it to production and keep it there: handling messy inputs, writing the evals that catch regressions, designing the guardrails, watching the cost per task, and knowing when AI is the wrong answer. That gap, between something that demos well and something that survives real traffic, is the difference a senior AI engineer makes. It is also exactly what we vet for.
What our AI engineers build
Across the modern AI stack, from the model to the production system around it.
LLM development
Build on Claude, GPT, Gemini and open models: RAG, fine-tuning, prompt-as-code, structured outputs and tool use.
AI agents
Multi-step agents that use your tools and take actions, with guardrails, evals and a human in the loop where it matters.
Natural language processing
Extraction, classification, summarisation and search over your documents, with citations and a checkable trail.
Computer vision
Detection, OCR, quality inspection and image understanding, trained and validated on your own data.
Machine learning
Models trained, deployed and monitored: forecasting, recommendation, scoring and classification at production scale.
MLOps & production
Evals, observability, cost control and CI/CD for models, so what ships keeps working as inputs and volume change.
Featured AI Experts
25 experts availableA hand-picked selection of 9 pre-vetted specialists.
Browse all 25 experts in the directory for the full network.
Yurii K.
LLM Engineer / GenAI Engineer
Oleh D.
AI/ML Engineer
Oleksandr S.
Computer Vision Engineer
Oleksandr N.
LLM Engineer / GenAI Engineer
Mykhailo R.
AI Engineer
Oleksandr S.
Voice AI Engineer
Vladyslav L.
Senior AI Engineer
Vitalii P.
Senior Big Data Engineer / Platform Engineer
Vladimir C.
Computer Vision Engineer
AI-Powered First-Line Support Agent (RAG)
AI Engineer
The project involved developing an intelligent automation system to handle Tier-1 customer inquiries for a microsite building platform. By leveraging advanced Retrieval-Augmented Generation (RAG), the system analyzes a knowledge base of over 280 articles to provide instant and accurate responses. The solution was designed to solve scalability challenges by automating repetitive, low-complexity questions that previously overwhelmed the human support team.
AI Agents Management & Orchestration Control Plane
AI Engineer
Developed a centralized, production-grade management platform (Control Plane) for the lifecycle management of specialized AI agents. The system provides a unified web interface for defining agent personas, deploying them across diverse environments, and monitoring their performance and token consumption in real-time.
AI-Powered Multi-Modal City Guide
AI Lead Engineer
Architected and launched a mobile-first, multi-modal AI travel companion that generates dynamic, location-aware storytelling. The platform utilizes generative AI to create historical narratives for points of interest, translates content in real-time, and synthesizes high-quality audio guides for a hands-free user experience.
Multi-Agent Personal Assistant & Time Organizer
AI Engineer
The project involved building a sophisticated personal productivity automation system based on a Multi-Agent Architecture to streamline tasks and professional collaboration. The system integrates with Google Workspace, Jira, Zoom, and Confluence to provide unified task management, automated scheduling, and intelligent email triage. By using a Supervisor Agent to orchestrate specialized sub-agents, the platform autonomously handles complex workflows like research and meeting coordination while maintaining human-in-the-loop security.
AI-Powered Creative Automation System
Lead AI Engineer / Architect
Creative teams lose days on manual marketing asset production, with inconsistent brand use, multi-platform resizing, and weak product-identity control. The system automates the pipeline from brief to delivery: it uses Computer Vision for design analysis, automated prompt engineering, fine-tuned generative models, smart one-to-many resizing (Saliency Maps), and ControlNet for product fidelity, with automated brand and compliance checks—cutting production from days to minutes while keeping full brand consistency.
Interrogation Transcription System for Law Enforcement
Voice AI Engineer
Automated real-time transcription of interviews to generate official protocols in a secure environment. On-premise (air-gapped) deployment ensuring maximum security and data privacy. Core Model: Python, OpenAI Whisper, Pyannote, Docker, on-premise deployment Orchestration: Custom system for real-time processing (voice detection + chunking + transcription). Supports up to 10 simultaneous sessions. Fine-tuning Pipeline: Created a pipeline for periodic model updates using client-provided datasets (edited transcripts). Focused on adapting to (local dialect) and low-quality audio. Metrics: Used WER (Word Error Rate) and CER (Character Error Rate) to validate model performance. Deployment: On-premise (Air-gapped). All components are deployed locally to ensure maximum security and data privacy.
Financial Voice Agent for Call Center
Voice AI Engineer
Voice agent integration for a financial services company with a focus on mobile stability. Focus: Integrated AI agents with telephony infrastructure. Solved architectural challenges regarding vendor integrations. Performance: Focused on maintaining high communication quality over mobile networks.
RAG for Medical equipment marketplace
AI Engineer
Knowledge base system for medical device documentation with semantic search capabilities. Pipeline: Web scraping of manufacturer manuals for specified medical devices → chunking → indexing with metadata → storage in vector database. Core Functionality: On query, retrieves relevant documentation and specifications for a given medical device.
Deepfake Voice Detection System
AI Solutions Architect
Telecom providers and financial institutions needed a solution to detect synthetic speech and protect against voice fraud in call centers. We built a real-time deepfake detection system for streaming audio using speaker recognition, diarization, ASR, Deep Fake detector model.
Our Technology Stack
Access talent across the full spectrum of modern technologies
Hire AI engineers: at a glance
- What
- Senior AI engineers across LLMs, ML, NLP, computer vision, agents and MLOps, added to your team.
- Experience
- Production experience, not just demos. Vetted through real delivery with evals and production discipline.
- How fast
- Integrated in about two weeks, because the bench is pre-vetted.
- Platforms
- Anthropic (Claude), Google Cloud (Vertex AI), Microsoft (Azure), plus the wider Python and ML stack.
- Track record
- 50+ AI projects delivered across regulated and high-volume work.
- Engagement
- Flexible, short or long term. No upfront recruitment fees on the augmentation model; you interview before choosing.
- Based in
- London-registered, top-rated UK AI firm on Clutch.
Common questions about hiring AI engineers
How quickly can AI engineers start?
Typically one to two weeks. The bench is pre-vetted, so we shortlist from people we have already proven rather than starting a search from scratch.
What is the minimum engagement?
Flexible, short or long term. We confirm the terms on the scoping call and you can scale up or down as the work changes.
Will the engineers overlap with our timezone?
Yes. The network spans CET and GMT and beyond, and we match working-hours overlap to your team so collaboration is real-time where it needs to be.
How do you ensure quality?
Vetted through real delivery: evals, production discipline and shipped work, plus a multi-stage technical screen. You also interview before choosing who joins.
Can we interview first?
Yes, always. We shortlist from the bench and the hiring decision stays with you.
Which platforms do your engineers work on?
Anthropic (Claude), Google Cloud (Vertex AI) and Microsoft (Azure), plus the wider Python, ML and data stack and full-stack web and mobile.
Hire an AI engineer who ships
Tell us the role and the outcome. We shortlist from the bench, you interview and choose, and the engineer integrates in about two weeks.