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.
Design and operate a secure, scalable AI platform infrastructure, building CI/CD pipelines and deployment workflows in collaboration with data science and engineering teams. Implement observability, automate infrastructure using IaC, and ensure high availability and compliance. Focus on performance, cost efficiency, and seamless integration of AI workloads and agents.
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.
Develop and optimize full-stack AI/ML systems for internal and external customers, focusing on MLOps pipelines, distributed training, and inference performance. Build open-source tools and reference architectures to scale AI workloads on NVIDIA platforms and cloud partners. Act as a technical advisor for complex production issues across hardware, software, and infrastructure layers.
Develop and optimize full-stack AI/ML systems, focusing on MLOps pipelines, distributed training, and inference performance across cloud and on-prem environments. Collaborate with internal teams and external customers to solve complex infrastructure challenges, particularly around LLMs and new hardware integration. Build open-source tools and reference architectures to scale AI workloads effectively.
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 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.
Develop and scale physical foundation models for AI-driven engineering simulation, bridging research and production. Work across the full stack from distributed training on multi-GPU cloud infrastructure to robust software implementation, collaborating with scientists and engineers to solve real-world physics problems. Contribute to reusable libraries and mentor team members in a high-impact, research-led environment.
Design and build scalable data systems for multi-modal scientific data, enabling efficient analysis and machine learning in a high-performance research environment. Focus on data pipeline architecture, storage optimization, and seamless integration between wet lab and ML teams. Work across cloud-native infrastructure to support large-scale, GPU-intensive model training and rapid iteration.
This role involves leading software development across AI and computer vision projects, spanning research, product, and service development. The engineer will contribute to all stages of the SDLC, from design to deployment, while working in multidisciplinary teams. Emphasis is placed on innovation, technical leadership, and applying expertise in low-TRL technology development.
