Senior AI Developer

We are looking for a highly skilled Senior AI Developer with strong hands-on experience in machine learning, deep learning, generative AI, MLOps, cloud AI platforms, NLP, computer vision, and production-grade AI deployment. This is a senior technical role suited to someone who has already built and deployed AI solutions in real-world business environments. The successful candidate will help design, build, deploy, and operate AI models and pipelines, while also contributing to the technical standards and foundations of a newly established AI function. The ideal candidate must be comfortable working in a greenfield environment, where they will be expected to work independently, solve complex technical problems, and help shape the client’s AI delivery capability.

Key Responsibilities AI Development and Engineering

● Design, build, train, test, and deploy machine learning and deep learning models.

● Develop end-to-end AI pipelines covering data ingestion, preprocessing, feature engineering, model training, evaluation, and deployment.

● Build scalable AI solutions that can operate reliably in production environments.

● Optimise models for performance, accuracy, scalability, and reliability.

● Work with structured and unstructured data to develop practical AI solutions.

● Translate business and technical requirements into AI-driven solutions. Generative AI and LLM Development

● Implement and fine-tune Large Language Models for enterprise use cases.

● Develop generative AI solutions using modern frameworks and tools.

● Design and build Retrieval-Augmented Generation architectures.

● Apply prompt engineering best practices.

● Work with embeddings, vector databases, semantic search, and document intelligence.

● Evaluate LLM performance, accuracy, hallucination risk, and output quality. MLOps and AI Infrastructure

● Establish and manage MLOps frameworks for production AI delivery.

● Implement model versioning, monitoring, retraining, performance tracking, and governance.

● Build automated AI/ML pipelines and deployment workflows.

● Implement CI/CD practices for machine learning and AI systems.

● Monitor production models for drift, degradation, and performance issues.

● Ensure AI solutions are secure, scalable, maintainable, and reliable. Cloud AI Platform Delivery

● Deploy and manage AI workloads on cloud platforms.

● Work with platforms such as Azure AI / Azure ML, AWS SageMaker, and Google Vertex AI.

● Support batch and real-time inference workloads.

● Integrate AI models with enterprise applications and data platforms.

● Ensure cloud-based AI deployments meet performance and reliability standards. NLP and Computer Vision

● Build NLP solutions including text processing, classification, named entity recognition, and transformer-based models.

● Develop computer vision solutions such as image classification, object detection, and segmentation.

● Deploy NLP and computer vision models into production environments where required. Collaboration and Technical Leadership

● Work directly with business and technical stakeholders.

● Contribute to AI technical standards, best practices, and delivery frameworks.

● Produce clear technical documentation, model cards, and deployment guides.

● Support knowledge transfer within the team.

● Mentor junior team members as the AI function grows.

● Operate independently and take ownership of AI solution delivery. Required Technical Skills Core AI / ML Skills

● Machine Learning

● Deep Learning

● Model training and optimisation

● Feature engineering

● Statistical modelling

● Experiment design

● Predictive modelling

● Production AI deployment Generative AI

● Large Language Models

● Prompt engineering

● Fine-tuning

● RAG architectures

● Generative AI applications

● Vector databases

● Semantic search

● Enterprise AI use cases MLOps

● ML pipeline automation

● Model versioning

● Model monitoring

● Model retraining

● CI/CD for ML

● Orchestration tools ● Model lifecycle management ● Production deployment practices Frameworks Strong production-level experience required in: ● TensorFlow ● PyTorch Cloud AI Platforms Experience with one or more of the following: ● Azure AI / Azure Machine Learning ● AWS SageMaker ● Google Vertex AI NLP ● Text processing ● Named entity recognition ● Text classification ● Transformer-based models ● NLP model deployment Computer Vision ● Image classification ● Object detection ● Image segmentation ● Production deployment of computer vision models Experience Requirements The ideal candidate should have: ● Minimum 7 years’ software development experience. ● At least 4 years’ experience focused on AI / ML engineering. ● Proven experience delivering AI or ML solutions into production. ● Experience working in enterprise or regulated environments. ● Strong experience with cloud-based AI deployment. ● Experience in greenfield or newly established AI environments would be advantageous. ● Ability to work independently and take ownership of technical delivery. ● Ability to define standards and build scalable AI foundations.

Qualifications Preferred qualifications: ● Degree in Computer Science, Data Science, Mathematics, Engineering, or a related technical field. Equivalent practical experience will also be considered. Advantageous certifications: ● Microsoft Azure AI / Azure ML certification ● AWS Machine Learning / SageMaker certification ● Google Cloud AI / Vertex AI certification ● Data Science or Machine Learning certification