Senior AI/ML Software Engineer | C#/.NET & Python

📍 Hybrid | 3 days per week on-site 🚀 About the Role We are looking for a Senior AI/ML Software Engineer to join a Framework and Research team responsible for developing shared services, reusable frameworks, and technical components used across multiple product areas. This role combines strong C#/.NET software engineering with hands-on Machine Learning and AI development. You will design backend services, prepare data, build and deploy different types of ML models, and help transform AI initiatives from proofs of concept into secure, scalable, and production-ready solutions. This is not purely an LLM or RAG position. We are looking for someone with practical experience building, training, evaluating, and deploying multiple types of Machine Learning models, such as classification, regression, time-series, anomaly detection, NLP, computer vision, or deep learning models. 🎯 Your Mission As a Senior AI/ML Software Engineer, you will: - Build robust backend services using C# and .NET. - Design, train, and deploy different types of Machine Learning models. - Develop reusable AI and ML components for multiple product areas. - Transform business requirements into production-ready software and AI solutions. - Contribute to LLM, RAG, and Generative AI initiatives without being limited to them. - Develop reliable ML pipelines using Python and modern modelling frameworks. - Apply MLOps practices to model deployment, versioning, monitoring, and retraining. - Help move AI projects from experimentation and proof of concept into production. - Mentor engineers and promote strong software and Machine Learning engineering practices. 💼 Key Responsibilities C#/.NET Software Engineering - Design, develop, and maintain backend services using C# and .NET/.NET Core. - Build scalable APIs, microservices, and shared technical components. - Translate business and technical requirements into reliable software solutions. - Apply clean code, software design patterns, testing, and architecture best practices. - Contribute to high-volume and performance-sensitive backend systems. - Integrate Machine Learning services into existing .NET applications and product ecosystems. - Participate in code reviews and technical design discussions. Machine Learning Development - Prepare, clean, transform, and validate datasets. - Define relevant features and select suitable modelling approaches. - Build, train, fine-tune, and evaluate Machine Learning models. - Work with different modelling techniques, including: - Classification - Regression - Natural Language Processing - Time-Series Forecasting - Anomaly Detection - Computer Vision - Deep Learning - Recommender Systems - Compare model performance and select solutions based on measurable results. - Document model choices, assumptions, limitations, and evaluation outcomes. AI & Generative AI Solutions - Develop AI-powered applications using Large Language Models. - Work with technologies and approaches such as: - GPT - Transformers - Hugging Face - Retrieval-Augmented Generation - Prompt Engineering - Integrate LLM capabilities into backend services and business applications. - Design appropriate guardrails and validation mechanisms. - Evaluate LLM outputs for relevance, reliability, safety, and performance. - Select traditional Machine Learning approaches instead of LLMs when they are better suited to the use case. ML Pipelines & Python Engineering - Develop production-grade ML pipelines using Python. - Work with frameworks such as: - PyTorch - TensorFlow - scikit-learn - Automate data preparation, feature engineering, model training, evaluation, and deployment. - Build reusable and maintainable components for ML development. - Apply software engineering practices to notebooks, scripts, pipelines, and model services. - Use Jupyter notebooks to explore data and rapidly validate new concepts. Model Deployment & MLOps - Deploy models through: - Cloud endpoints - REST APIs - Batch processing pipelines - Edge applications - Implement model versioning and experiment tracking. - Contribute to model registries and controlled promotion between environments. - Monitor model performance, prediction quality, data quality, and service behaviour. - Support model retraining and recalibration processes when required. - Detect and respond to: - Model drift - Data drift - Performance degradation - Unexpected prediction behaviour - Ensure ML solutions remain reliable after production deployment. Cloud AI Engineering - Design and implement cloud-based AI and ML architectures. - Deploy and operate Machine Learning solutions using cloud services such as: - Azure Machine Learning - Azure AI - AWS SageMaker - AWS Bedrock - Google Vertex AI - Select appropriate cloud services based on scalability, security, performance, and operational requirements. - Integrate cloud AI capabilities with existing applications and data platforms. - Support cloud resource configuration, deployment, and troubleshooting. Containers & Orchestration - Containerize applications and Machine Learning services using Docker. - Deploy and operate workloads in Kubernetes environments. - Ensure model services are scalable, observable, and resilient. - Support configuration, resource management, health checks, and deployment automation. - Collaborate with DevOps and platform teams on production architecture. CI/CD & Delivery Automation - Contribute to CI/CD pipelines for software and Machine Learning components. - Automate build, testing, validation, packaging, and deployment activities. - Integrate model validation and quality checks into delivery pipelines. - Ensure changes are traceable, tested, and safely deployed. - Promote repeatable release processes across development and production environments. Production Monitoring & Support - Monitor backend services, ML pipelines, and deployed models. - Troubleshoot production incidents and performance issues. - Support services in line with agreed SLAs. - Perform root cause analysis and implement sustainable corrections. - Identify technical debt and contribute to remediation plans. - Improve logging, alerting, tracing, and operational visibility. AI Governance, Security & Privacy - Apply security, privacy, and responsible AI practices throughout the AI lifecycle. - Protect sensitive data used for training, evaluation, and inference. - Contribute to access controls, API security, auditability, and traceability. - Implement appropriate AI guardrails and validation mechanisms. - Document model limitations, risks, and intended use. - Ensure solutions follow applicable governance and internal compliance requirements. Technical Leadership & Collaboration - Work closely with Product Owners and cross-functional engineering teams. - Support functional and technical decisions throughout the delivery lifecycle. - Conduct code reviews and promote engineering best practices. - Document architecture decisions, model designs, and operational procedures. - Mentor other engineers and support knowledge sharing across the team. - Contribute to Agile ceremonies, estimations, planning, and continuous improvement. ✅ What We’re Looking For Mandatory Requirements - Bachelor’s or Master’s degree in: - Computer Science - Software Engineering - Data Science - Artificial Intelligence - A related technical field - At least 5 years of professional software engineering experience. - At least 5 years of hands-on experience with C# and .NET/.NET Core. - At least 3 years of hands-on experience in Machine Learning, AI Engineering, or Data Science. - Strong Python skills for: - Data processing - Model development - ML pipelines - Automation - Proven experience building, training, and deploying at least two different types of ML models beyond LLM and RAG solutions. - Practical experience with one or more of the following: - PyTorch - TensorFlow - scikit-learn - Experience deploying Machine Learning models into production. - Experience with model evaluation, experiment tracking, model versioning, and production monitoring. - Experience deploying ML solutions using Azure, AWS, or Google Cloud. - Strong knowledge of Docker and Kubernetes. - Experience with CI/CD pipelines and MLOps practices. - Experience with NLP, Transformers, Hugging Face, LLMs, or prompt engineering. - Good understanding of API security, AI governance, and data privacy. - Experience with microservices, databases, and high-volume backend systems. - Fluent English. - Experience working within Agile or Scrum teams. 🛠 Technical Stack Software Engineering - C# - .NET - .NET Core - REST APIs - Microservices - Backend Architecture - High-Volume Systems Machine Learning & Data Science - Python - PyTorch - TensorFlow - scikit-learn - pandas - NumPy - Jupyter Modelling - Classification - Regression - Time-Series Forecasting - Anomaly Detection - Natural Language Processing - Computer Vision - Deep Learning - Recommender Systems Generative AI - Large Language Models - GPT - Transformers - Hugging Face - Retrieval-Augmented Generation - Prompt Engineering - AI Guardrails Cloud AI Platforms - Azure Machine Learning - Azure AI - AWS SageMaker - AWS Bedrock - Google Vertex AI MLOps - ML Pipelines - Experiment Tracking - Model Versioning - Model Registries - Model Monitoring - Drift Detection - Automated Retraining Cloud-Native Delivery - Docker - Kubernetes - CI/CD - APIs - Batch Processing - Cloud Endpoints - Edge Deployment ⭐ Nice to Have - Experience monitoring model drift and data drift. - Experience implementing automated retraining processes and performance alerts. - Knowledge of feature stores, experiment-tracking tools, and model registries. - Strong experience in one or more specialist areas: - Computer Vision - Recommender Systems - Anomaly Detection - Time-Series Forecasting - Cloud, Machine Learning, or AI certifications. - Experience working with regulated, sensitive, or privacy-restricted data. - Experience implementing AI governance and responsible AI controls. - Knowledge of Java and Spring Boot. - Experience supporting ML services with strict performance or availability requirements. 🤝 Soft Skills Technical Ownership - Ability to take ownership from initial concept through production delivery. - Comfortable balancing software engineering, modelling, and operational concerns. - Strong focus on sustainable and production-ready solutions. Analytical Thinking - Ability to select appropriate modelling approaches based on the problem and available data. - Strong critical-thinking and experimentation skills. - Comfortable challenging assumptions and evaluating technical trade-offs. Communication & Collaboration - Ability to explain complex AI and ML concepts clearly. - Strong collaboration with Product Owners, Software Engineers, Data Scientists, and platform teams. - Confidence documenting and presenting technical decisions. Leadership & Mentoring - Ability to guide other engineers through code reviews and technical discussions. - Willingness to share knowledge and promote engineering standards. - Strong commitment to team development and continuous improvement. Quality & Responsibility - Attention to software quality, model performance, security, and privacy. - Awareness of the risks associated with production AI systems. - Rigorous approach to testing, documentation, and operational monitoring.