As a Senior AI Engineer at PoliceAI, you will lead the design, development, and deployment of AI-enabled products, translating policing needs into technical solutions. You will work with multidisciplinary teams to build secure, maintainable, and scalable services, and support the testing, evaluation, and validation of AI products.
As Head of AI, you will lead the development and implementation of AI strategies, governance, and capabilities at LPPA, a leading pension administration company. You will work closely with the CTO and executive team to identify and deliver AI opportunities that enhance operational efficiency, service quality, and member outcomes. Key responsibilities include building the AI team, establishing governance frameworks, and fostering AI adoption across the organization.
This role involves owning and developing the MLOps infrastructure for a growing AI platform, building and maintaining orchestration pipelines, deploying and managing ML workloads on Kubernetes and cloud platforms, and working closely with data scientists and AI teams to scale deployments.
This role involves configuring and optimizing Databricks-based ML development and deployment frameworks, implementing model training, deployment, monitoring, and governance capabilities, and building CI/CD pipelines for ML lifecycle management. You will lead the technical implementation of pilot models and develop reusable migration accelerators and standards.
This role involves configuring and maintaining AWS infrastructure, building CI/CD pipelines, automating deployment tasks, and supporting the packaging and deployment of machine learning models. You will work closely with developers to ensure consistent and reliable production systems.
This role involves supporting the configuration and maintenance of AWS infrastructure, building and maintaining CI/CD pipelines, automating deployment and operational tasks, and packaging and deploying machine learning models. You will work closely with software and AI engineers to ensure production systems operate effectively and reliably.
As a Senior MLOps Engineer, you will design and maintain scalable MLOps infrastructure, deploy and manage machine learning models using Azure ML and AKS, and build CI/CD pipelines. You will also implement observability and monitoring frameworks, and collaborate with engineering and delivery teams to support AI solutions.
This role involves deploying, tuning, and managing multi-rack GPU clusters and high-performance networks, working closely with researchers and engineers to optimize system performance. You'll handle everything from hardware to the orchestration layer, ensuring the compute infrastructure supports responsible AI development.
This role involves designing and maintaining the MLOps and deployment infrastructure that enables data scientists to transition machine learning models from exploration to production. The engineer will work across AWS, Azure, and GCP to build scalable, containerised systems using Kubernetes and infrastructure-as-code tools. The focus is on creating reliable, secure, and high-performance platforms that support client-facing AI solutions.
Design and implement production-grade AI and ML systems with a focus on Agentic AI, RAG, and LangGraph/LangChain patterns. Drive engineering standards, observability, and safe deployment of complex AI solutions across multiple teams. Work in a regulated environment to establish MLOps best practices and support AI-assisted development workflows.
This role involves designing, developing, and deploying secure AI and ML solutions for autonomous and mission-critical systems. Responsibilities include building models, supporting autonomous capabilities, and ensuring adherence to Responsible AI and safety standards.
This role involves designing and building production-grade data and AI systems on the Databricks platform in close collaboration with enterprise customers. Forward Deployed Engineers lead technical architecture, implement end-to-end solutions combining data pipelines and ML/AI models, and ensure integration with client systems. The role emphasizes customer immersion, reusable asset development, and translating complex technical challenges into scalable, secure solutions.
This role involves leading the design and implementation of end-to-end data and AI solutions for enterprise customers using the Databricks platform. The Senior Architect will work hands-on to build production-grade systems, integrate ML/AI models, and deliver scalable data architectures while collaborating closely with technical and business stakeholders. The role emphasizes customer immersion, reusable asset development, and influencing product strategy through real-world deployment insights.
This role involves designing, developing, and deploying AI/ML applications using Python, PyTorch, and GCP. You will build and integrate generative AI solutions, containerize and serve machine learning models, and implement CI/CD pipelines and model monitoring. Collaboration with engineering teams and stakeholders is key.
The role involves designing and building automated services and workflows to move AI models from development through testing to production release. It requires close collaboration across multiple engineering teams to integrate systems and improve the reliability and transparency of the model-release lifecycle. The engineer will use AI-assisted development to automate manual tasks and enhance delivery speed.
This role involves leading software development projects, participating in multidisciplinary teams, and providing SME expertise in AI and Computer Vision. Responsibilities include project planning, software design, development, testing, and deployment, as well as contributing to continuous improvement and technical documentation.
