Job Description

About TechLabs London:At TechLabs London, we build cutting-edge digital products that power smarter, more connected organizations. Our flagship solution is transforming housing management with modern cloud technology, intuitive design, and seamless integration. We are now embedding AI and intelligent automation across our platforms — and this role is at the center of that mission. We’re looking for a driven and creative AI Engineer to lead this charge with us, to deliver production-ready AI features, predictive analytics, and computer vision tools that unlock real business value.  Job Purpose: As an AI Engineer you’ll be responsible for the development, deployment, and optimisation of advanced AI and ML capabilities within TechLabs London. You will collaborate with cross-functional teams to embed intelligent solutions into our existing product suite and shape the next generation of AI-powered features across our platforms. This role is perfect for someone who’s passionate about building practical AI solutions and wants to see their work improve lives, save time, and power decisions. You’ll help the business translate complex challenges into intelligent, scalable solutions.  Responsibilities:

• Design and build end-to-end AI pipelines across use cases such as predictive modelling, NLP, computer vision, and conversational AI.
• Design, develop and deploy conversational AI solutions using LLMs and agentic frameworks (e.g. LangChain, LangGraph), including RAG pipelines, intent classification, entity extraction, and multi-turn dialogue management.
• Apply computer vision to extract intelligence from images and videos for detection, classification, and automation.
• Develop time-series models that analyze behavior and generate predictive.
• Experiment with foundational models and fine-tune them for specific tasks relevant to our domain.
• Integrate models into production by working with our developers to deploy solutions into our products and beyond.
• Architect and build scalable, secure, and reusable ML APIs, workflows, and pipelines using modern MLOps practices and containerisation (Docker).
• Translate business problems into data-driven solutions, working closely with product, customer success, and engineering teams.
• Champion AI-first thinking across the company, supporting POCs, internal tooling, and customer-facing features.
• Collaborate with internal developers, product managers, domain experts, and external stakeholders to ensure solutions are feasible, maintainable, and impactful, as well as to define data strategies and model objectives.
• Conduct rigorous A/B testing and model evaluation.
• Support the integration of models into production systems using APIs and microservices.
• Stay abreast of advancements in AI/ML research and apply relevant breakthroughs to ongoing projects.

  Skills and Experience:

• Bachelor’s degree in information technology, Software Engineering, Computer Science, or related field.
• Proven experience building and deploying LLM-powered applications, including RAG pipelines and conversational agents.
• Hands-on experience with LangChain or similar agentic frameworks (LangGraph, AutoGen, or equivalent).
• Strong proficiency in Python and relevant ML libraries (e.g. pandas, scikit-learn, PyTorch, spaCy, Hugging Face, OpenCV etc.)
• Experience with cloud platforms — Azure is our primary platform (preferred); AWS or GCP also considered.
• Containerization and orchestration: Docker, Docker Compose.
• Solid understanding of data modelling, feature engineering, and data warehousing concepts.
• Strong knowledge of SQL and experience with relational and/or vector databases (e.g. pgvector, Pinecone, Weaviate).
• Proficiency in version control, CI/CD, and model deployment practices.
• Experience integrating AI models into web or mobile applications is highly valued.
• Experience with MLOps tools like MLflow, Airflow, DVC.
• Familiarity with MCP Server (Model Context Protocol) for building tool-augmented AI agents is a plus.

  Mindset & Soft Skills:

• Strong problem-solving and analytical thinking — able to translate messy real-world data into measurable solutions.
• Pragmatic and delivery-oriented — capable of balancing technical innovation with business impact.
• Communicative and collaborative — comfortable working across teams and disciplines.
• Self-driven — able to manage time, priorities, and project ownership in a fast-moving team.
• Knowledge of data governance, bias mitigation, and explainable AI techniques.

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