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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Solutions Engineer - **Company:** Techgroove Limited - **Location:** Basingstoke, UK (Remote available) - **Experience:** Experienced - **Salary:** £55,000.0 - £60,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Computer Vision, Microsoft Azure, Cloud Computing, Code Review, Continuous Integration, Data Cleansing, Django Web Framework, Github, Python (Programming Language), PostgreSQL, Machine Learning, Object Detection, Redis, Tensorflow, Azure Machine Learning, Software Safety, Software Engineering, Data Streaming, Google Cloud, Pytorch, Retrieval-Augmented Generation, Flask (Web Framework), Large Language Models, Prompt Engineering, Fastapi, AI Platforms, Scikit Learn, Kubernetes, Information Technology, HuggingFace, Apache Kafka, Machine Learning Operations, Api Design, Restful APIs, Terraform, Software Version Control, Docker, Microservices - **Published:** July 19, 2026 - **Apply:** https://uk.indeed.com/viewjob?jk=a6c805e2b724e9e5 ## About the Role * Bachelor's or Master's degree in Computer Science, AI, Mathematics, or a closely related field (or equivalent demonstrable experience) * 2-5 years of hands-on software engineering or AI/ML engineering experience in production environments * Strong Python proficiency - clean, testable, well-documented code by default * Practical experience building with LLM APIs: OpenAI, Anthropic, Azure OpenAI, or comparable * Experience with RAG architectures, vector databases (Pinecone, Weaviate, pgvector, Chroma), and embedding models * Familiarity with ML frameworks: TensorFlow, PyTorch, scikit-learn, Hugging Face Transformers * Experience with REST API development (FastAPI, Flask, Django REST) and microservices architectures * Hands-on cloud experience on at least one of AWS, Azure, or GCP, particularly AI/ML service offerings * Understanding of MLOps principles: model versioning, CI/CD for ML, experiment tracking, and model monitoring * Strong communication skills - able to explain complex AI concepts to non-technical stakeholders clearly and confidently Preferred * Experience building agentic AI systems using LangChain, LlamaIndex, AutoGen, or similar orchestration frameworks * Knowledge of fine-tuning LLMs (LoRA, QLoRA, PEFT) and prompt engineering best practices * Experience with data streaming technologies: Kafka, Azure Event Hub, or AWS Kinesis * Familiarity with AI evaluation frameworks and responsible AI practices (bias detection, fairness metrics, explainability) * Experience in regulated industries: financial services, healthcare, or public sector * MLOps tooling: MLflow, Kubeflow, Weights & Biases, or Azure ML pipelines * Understanding of computer vision approaches: object detection, image classification, document digitisation * AWS Certified Machine Learning - Specialty, AWS AI Practitioner, Microsoft Azure AI Engineer Associate, Google Professional Machine Learning Engineer, or DeepLearning.AI certifications (we sponsor certification study for the right candidates) Tools & Technologies * Languages/Frameworks: Python, FastAPI, LangChain, LlamaIndex * AI/ML: OpenAI API, Anthropic Claude, TensorFlow, PyTorch, Hugging Face, MLflow * Vector Databases: Pinecone, Weaviate * Cloud: AWS, Azure, GCP * Infrastructure: Docker, Kubernetes, Terraform, GitHub Actions * Data: PostgreSQL, Redis ## Description Techgroove is seeking a talented and ambitious AI Solutions Engineer to join our growing AI Engineering team. This is a pivotal role in our company's strategic shift towards becoming a full AI powerhouse. You will sit at the intersection of cutting-edge AI research and real-world business delivery - designing, building, and deploying intelligent solutions that create tangible, measurable value for our enterprise clients. You will work across the full solution lifecycle, from client discovery through architecture, deployment, and ongoing reliability, alongside data scientists, cloud architects, and business consultants in cross-functional squads., Solution Design & Delivery * Design, develop, and deploy end-to-end AI solutions using large language models (LLMs), machine learning frameworks, and cloud AI services * Architect and implement RAG (Retrieval-Augmented Generation) systems, AI agents, and conversational AI pipelines tailored to client requirements * Develop proof-of-concept AI applications that demonstrate business value quickly and clearly * Conduct technical discovery sessions with clients to understand requirements and translate them into AI architectural designs AI & MLOps * Build and optimise ML pipelines including data preprocessing, model training, evaluation, deployment, and monitoring * Integrate AI models and APIs (OpenAI, Anthropic Claude, Azure OpenAI, Hugging Face) into client applications and workflows * Optimise AI systems for performance, cost efficiency, and scalability in cloud environments * Collaborate with data scientists and ML engineers to translate research prototypes into robust, production-ready systems Safety & Responsible AI * Implement AI safety, security, and responsible AI practices including bias testing, explainability tooling, and access controls Collaboration & Documentation * Participate in code reviews, architecture decisions, and technical mentoring of junior engineers * Document AI architectures, model cards, and operational runbooks to ensure maintainability and knowledge transfer * Stay at the forefront of AI research - evaluating and recommending new tools, frameworks, and approaches relevant to client needs ## Related Videos - [Navigating the AI Revolution in Software Development](https://www.wearedevelopers.com/videos/1266-navigating-the-ai-revolution-in-software-development) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Reducing LLM Calls with Vector Search Patterns - Raphael De Lio (Redis)](https://www.wearedevelopers.com/videos/1714-reducing-llm-calls-with-vector-search-patterns-raphael-de-lio-redis) - [Bringing AI Model Testing and Prompt Management to Your Codebase with GitHub Models](https://www.wearedevelopers.com/videos/1536-bringing-ai-model-testing-and-prompt-management-to-your-codebase-with-github-models) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)