9 of 9 Feature Engineering Jobs in the South East

cutting-edge project, driving innovation through Large Language Models (LLMs), GenAI, and predictive analytics. Key Responsibilities: Design, develop, and deploy AI/ML models, including LLMs and GenAI. Conduct feature engineering, model optimization, and performance tuning. Implement and train supervised, unsupervised, and reinforcement learning models. Carry out advanced data exploration, analysis, and preprocessing. Deploy scalable models into production More ❯

success criteria. Support and mentor junior team members, fostering a collaborative environment. Independently manage client relationships and project expectations. Develop and deploy ML models, applying innovative approaches beyond basic feature engineering. Use Python, SQL, and Tableau for reporting and analysis within an AWS environment. Work closely with Data Engineering, Sales, and Market Research teams across the UK, US … meaningful insights to drive business decisions. Strong communication skills, with experience in managing client relationships. A degree in a relevant field such as Mathematics, Statistics, Economics, Psychology, Sociology, Physics, Engineering, or Computer Science (Master’s or PhD is a plus). Interest or experience in TV viewing data, media, or marketing analytics is beneficial but not essential. If this More ❯

learning algorithms and frameworks. Proven experience with Python, TensorFlow, PyTorch, or similar tools. Expertise in developing and deploying machine learning models in a production environment. Experience with data preprocessing, feature engineering, and model evaluation. Proficiency in handling large datasets and optimizing model performance. Knowledge of cloud platforms such as AWS, GCP, or Azure. Excellent English communication skills (written … include: Leading the development and deployment of machine learning models. Collaborating with cross-functional teams to understand business requirements and translate them into technical solutions. Conducting data analysis and feature engineering to improve model accuracy. Implementing scalable machine learning solutions on cloud platforms. Keeping abreast of the latest developments in AI and machine learning to incorporate best practices More ❯

platforms using Dataproc, Dataflow, Pub/Sub, BigQuery, Cloud Spanner, and Bigtable. Define and implement real-time and batch processing pipelines for complex use cases involving streaming analytics, ML feature engineering, and automation. Act as a trusted advisor to senior technical and business stakeholders across industries (e.g., telecom, retail, financial services). Drive data quality, governance, lineage, and … security standards across enterprise data pipelines. Mentor engineering teams and lead best practice adoption across data architecture, orchestration, and DevOps tooling. Participate in technical workshops, executive briefings, and architecture reviews to evangelize GCP data capabilities. Required Qualifications Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related technical field. 12+ years of experience in data … architecture and data engineering with proven skills and leadership in large-scale cloud data programs. 5+ years of advanced expertise in Google Cloud data services: Dataproc, Dataflow, Pub/Sub, BigQuery, Cloud Spanner, and Bigtable. Hands-on experience with orchestration tools like Apache Airflow or Cloud Composer. Hands-on experience with one or more of the following GCP data More ❯

Principal ML/AI Engineer - Python | Greenfield AI Platform (Contract) We are seeking a Principal/Senior AI/ML Engineer with a strong foundation in software engineering and Python development, to join a globally renowned organisation at the forefront of AI innovation. This is a rare chance to work on greenfield AI/ML projects, delivering transformative solutions … systems using Python and modern frameworks. Lead technical design discussions and influence system-level decisions across greenfield initiatives. Build and scale end-to-end ML pipelines, including data ingestion, feature engineering, model training, and deployment. Champion best practices in software engineering: CI/CD, testing, containerisation, version control, and code quality. Collaborate with cross-functional teams including … data scientists, platform engineers, and product stakeholders to align AI solutions with business goals. Experience Strong commercial experience in Python with a deep understanding of software engineering principles. Demonstrated expertise in building ML systems in production environments (not just notebooks or research). Proven leadership experience in either a senior IC or tech lead/managerial capacity. Familiarity with More ❯

grow by tackling complex technical challenges. Responsibilities Design, build, and maintain AI systems and tools using machine learning and large language models (LLMs), with a strong focus on prompt engineering and model fine-tuning. Collaborate with cross-functional teams to identify opportunities for AI and automation across the product suite. Develop scalable pipelines for data ingestion, model training, evaluation … operating as a senior level engineer within a product or SaaScompany Proven experience with Python and ML libraries such as TensorFlow or PyTorch. Experience working with LLMs (Gemini), prompt engineering, and reinforcement learning from human feedback (RLHF). Experience with LangChain for building LLM applications with RAG pipelines and agent workflows. Practical understanding of vector search, embeddings, and retrieval … generation (RAG). Experience building and deploying machine learning models into production environments. Familiarity with MLOps tools and practices (Vertex AI). Understanding of data pipelines, data preparation, and feature engineering. Bonus Points for: Hands-on experience with prompt tuning, fine-tuning LLMs, or adapting open-source models for specific use cases. Hands-on experience with AlloyDB or similar. More ❯

skills – even better if these skills have been applied to threat detection, malware analysis, phishing and/or abuse detection. Experience building production-grade AI pipelines, including data ingestion, feature engineering, validation, model deployment, and monitoring. Experience designing and implementing anomaly detection, classification, clustering, and retrieval across vision and language models, ideally for identifying cyber threats (URLs, domains … fit to problem space, including scenarios where RAG is applicable. Incident response experience, and ability to work with large, noisy, and rapidly evolving threat datasets. Strong background in cloud engineering and containerisation (Docker, Kubernetes) with experience deploying AI services at scale, particularly on AWS via Terraform. The package offered to the Senior/Principal AI Engineer will consist of More ❯

record of transitioning research into production. Proven team leadership in a technical or research setting. Expert in Python and PyTorch; JAX is a plus. Strong foundation in data preparation, feature engineering, and model evaluation. Excellent communicator with the ability to adapt messaging for different audiences. Strategic mindset and leadership potential at the executive level. Desirable: Background in analog … or general electronic engineering is a bonus. Experience with high-performance or low-latency ML systems. If you’re a principle machine learning engineer/team leader, or hold the relevant experience and are interested in this exciting opportunity, please do apply! If you are interested in another other AI/ML and Computer vision roles, please reach out More ❯

You have experience optimising solution performance with a constrained set of technologies. You have experience or are keen to engage with productionising machine learning technologies combined with large scale feature engineering. More ❯