Initialize IT
PingFederate Engineer/Consultant - London/hybrid - 6 - 9 months £485 per day Experience Typically 4-8+ years in IAM/Access Management/Identity Federation roles. Proven experience working in UK-regulated or compliance-driven environments . Experience working onshore with UK clients or stakeholders . Exposure to cloud or hybrid identity architectures (Azure AD, AWS, GCP) is desirable Desirable (not mandatory): Ping Identity certifications Security or IAM certifications (CISSP, CISM, Security+, etc.) Experience delivering Financial Services or Insurance IAM programmes in the UK. CIAM (Customer Identity) exposure. Migration experience from ADFS, SiteMinder, or other Legacy federation platforms to PingFederate. Familiarity with ServiceNow, JIRA, Confluence , and structured delivery models. Role You will eb responsible for the design, implementation, and operational support of enterprise-grade federated identity and single sign-on (SSO) solutions using PingFederate. The role is client-facing , requiring close collaboration with UK-based stakeholders, application teams, and security leadership to deliver secure, compliant authentication services across on-premise, cloud, and hybrid environments. This role operates within regulated UK environments (eg Financial Services, Insurance, Healthcare), ensuring adherence to UK security standards, data protection requirements, and audit expectations while enabling modern authentication using SAML, OAuth, and OpenID Connect. Key Responsibilities UK Onshore Delivery & Stakeholder Engagement Act as a UK onshore technical SME for PingFederate within client IAM programmes. Engage directly with UK business, application, and security stakeholders to gather requirements and explain federation designs in clear, non-vendor language. Participate in design authorities, CABs, and security reviews , representing the identity federation domain. Support UK delivery governance, documentation standards, and change management processes. Identity Federation & SSO Engineering Design, configure, and support PingFederate as an Identity Provider (IdP) and Service Provider (SP) for internal, partner, and customer applications. Implement and support SAML 2.0, OAuth 2.0, and OpenID Connect (OIDC) authentication flows. Deliver IdP-initiated and SP-initiated SSO integrations for web, mobile, and API-based services. Integrate PingFederate with Active Directory/LDAP and external identity providers. Platform Administration & Operations Perform installation, configuration, upgrade, and patching of PingFederate in line with UK enterprise standards. Configure high availability, clustering, and load-balanced deployments suitable for regulated production environments. Manage SSL/TLS certificates , key rotation, trust stores, and federation metadata. Provide 3rd-line support , root-cause analysis, and incident resolution for authentication and federation issues. Support change, release, and incident processes aligned to ITIL practices. Security, Risk & Compliance Ensure solutions comply with UK regulatory and security requirements , including: Data protection and privacy obligations (eg UK GDPR) Internal security policies and audit controls Implement MFA and adaptive authentication integrations (eg PingID or third-party MFA). Apply Zero Trust and least-privilege principles within federation and token designs. Support internal and external audits by providing technical evidence and documentation. Customisation & Integration Develop or support custom PingFederate adapters, selectors, and token translators (Java-based) where required. Integrate PingFederate with PingAccess, API gateways, CIAM platforms , and cloud identity services. Work with DevOps teams to support automation, environment consistency, and controlled deployments
PingFederate Engineer/Consultant - London/hybrid - 6 - 9 months £485 per day Experience Typically 4-8+ years in IAM/Access Management/Identity Federation roles. Proven experience working in UK-regulated or compliance-driven environments . Experience working onshore with UK clients or stakeholders . Exposure to cloud or hybrid identity architectures (Azure AD, AWS, GCP) is desirable Desirable (not mandatory): Ping Identity certifications Security or IAM certifications (CISSP, CISM, Security+, etc.) Experience delivering Financial Services or Insurance IAM programmes in the UK. CIAM (Customer Identity) exposure. Migration experience from ADFS, SiteMinder, or other Legacy federation platforms to PingFederate. Familiarity with ServiceNow, JIRA, Confluence , and structured delivery models. Role You will eb responsible for the design, implementation, and operational support of enterprise-grade federated identity and single sign-on (SSO) solutions using PingFederate. The role is client-facing , requiring close collaboration with UK-based stakeholders, application teams, and security leadership to deliver secure, compliant authentication services across on-premise, cloud, and hybrid environments. This role operates within regulated UK environments (eg Financial Services, Insurance, Healthcare), ensuring adherence to UK security standards, data protection requirements, and audit expectations while enabling modern authentication using SAML, OAuth, and OpenID Connect. Key Responsibilities UK Onshore Delivery & Stakeholder Engagement Act as a UK onshore technical SME for PingFederate within client IAM programmes. Engage directly with UK business, application, and security stakeholders to gather requirements and explain federation designs in clear, non-vendor language. Participate in design authorities, CABs, and security reviews , representing the identity federation domain. Support UK delivery governance, documentation standards, and change management processes. Identity Federation & SSO Engineering Design, configure, and support PingFederate as an Identity Provider (IdP) and Service Provider (SP) for internal, partner, and customer applications. Implement and support SAML 2.0, OAuth 2.0, and OpenID Connect (OIDC) authentication flows. Deliver IdP-initiated and SP-initiated SSO integrations for web, mobile, and API-based services. Integrate PingFederate with Active Directory/LDAP and external identity providers. Platform Administration & Operations Perform installation, configuration, upgrade, and patching of PingFederate in line with UK enterprise standards. Configure high availability, clustering, and load-balanced deployments suitable for regulated production environments. Manage SSL/TLS certificates , key rotation, trust stores, and federation metadata. Provide 3rd-line support , root-cause analysis, and incident resolution for authentication and federation issues. Support change, release, and incident processes aligned to ITIL practices. Security, Risk & Compliance Ensure solutions comply with UK regulatory and security requirements , including: Data protection and privacy obligations (eg UK GDPR) Internal security policies and audit controls Implement MFA and adaptive authentication integrations (eg PingID or third-party MFA). Apply Zero Trust and least-privilege principles within federation and token designs. Support internal and external audits by providing technical evidence and documentation. Customisation & Integration Develop or support custom PingFederate adapters, selectors, and token translators (Java-based) where required. Integrate PingFederate with PingAccess, API gateways, CIAM platforms , and cloud identity services. Work with DevOps teams to support automation, environment consistency, and controlled deployments
Initialize IT
ML Ops engineer with Data Science background (AWS services) - London/remote - £536 per day ML Ops engineer with experience i n data science, DevOps, and AWS SageMaker, along with a solid understanding of Agile software development principles. In this role, you will act as a bridge between Data Scientists and IT DevOps Engineers, helping translate experimental ML models into scalable, production-ready applications. You'll play a critical role in building practical solutions to real-world data science challenges, including automating workflows, packaging models, and deploying them as microservices using AWS services . The ideal candidate will be adept at developing end-to-end applications to serve AI/ML models, including those from platforms like Hugging Face, and will work with a modern AWS-based toolchain (SageMaker, Fargate, Bedrock). Your core responsibilities include: Serve as the day-to-day liaison between Data Science and DevOps, ensuring effective deployment and integration of AI/ML solutions using AWS services. Assist DevOps engineers with packaging and deploying ML models, helping them understand AI specific requirements and performance nuances. Design, develop, and deploy standalone and micro-applications to serve AI/ML models, including Hugging Face Transformers and other pre-trained architectures. Build, train, and evaluate ML models using services such as AWS SageMaker, Bedrock, Glue, Athena, Redshift, and RDS. Help create the knowledge artefacts for Data Scientist around DevOps and ML Ops. Where required, hand hold the data scientist and assist them with DevOps engineering issues, package installation issues, creating a Docker container, ML Ops tooling issues. Develop and expose secure APIs using Apigee, enabling easy access to AI functionality across the organization. Manage the entire ML life cycle-from training and validation to versioning, deployment, monitoring, and governance. Build automation pipelines and CI/CD integrations for ML projects using tools like Jenkins and Maven. Solve common challenges faced by Data Scientists, such as model reproducibility, deployment portability, and environment standardization. Assist the product owner to define and implement the ML Ops roadmap. Support knowledge sharing and mentorship across data Scientists teams, promoting a best practice-first culture. What skills are required? Minimum skills: Degree in computer science, economics, data science or another technical field (eg maths, physics, statistics etc.), or equivalent relevant experience Strong programming proficiency in Python (or R), with practical experience in machine learning and statistical modelling. Proven experience delivering end-to-end data science products, including both experimentation and deployment. Solid understanding of data cleaning, feature engineering, and model performance evaluation. Essential skills: Demonstrated experience deploying and maintaining AI/ML models in production environments. Hands-on experience with AWS Machine Learning and Data services: SageMaker, Bedrock, Glue, Kendra, Lambda, ECS Fargate, and Redshift. Familiarity with deploying Hugging Face models (eg, NLP, vision, and generative models) within AWS environments. Ability to develop and host microservices and REST APIs using Flask, FastAPI, or equivalent frameworks. Proficiency with SQL, version control (Git), and working with Jupyter or RStudio environments. Experience integrating with CI/CD pipelines and infrastructure tools like Jenkins, Maven, and Chef. Strong cross-functional collaboration skills and the ability to explain technical concepts to non technical stakeholders. Ability to work across cloud-based architectures. Tools & Technologies: AWS Services: SageMaker, Bedrock, Glue, ECS Fargate, Athena, Kendra, RDS, Redshift, Lambda, CloudWatch Other Tooling: Apigee, Hugging Face, RStudio, Jupyter, Git, Jenkins, Linux Languages & Frameworks: Python, R, Flask, FastAPI, SQL
ML Ops engineer with Data Science background (AWS services) - London/remote - £536 per day ML Ops engineer with experience i n data science, DevOps, and AWS SageMaker, along with a solid understanding of Agile software development principles. In this role, you will act as a bridge between Data Scientists and IT DevOps Engineers, helping translate experimental ML models into scalable, production-ready applications. You'll play a critical role in building practical solutions to real-world data science challenges, including automating workflows, packaging models, and deploying them as microservices using AWS services . The ideal candidate will be adept at developing end-to-end applications to serve AI/ML models, including those from platforms like Hugging Face, and will work with a modern AWS-based toolchain (SageMaker, Fargate, Bedrock). Your core responsibilities include: Serve as the day-to-day liaison between Data Science and DevOps, ensuring effective deployment and integration of AI/ML solutions using AWS services. Assist DevOps engineers with packaging and deploying ML models, helping them understand AI specific requirements and performance nuances. Design, develop, and deploy standalone and micro-applications to serve AI/ML models, including Hugging Face Transformers and other pre-trained architectures. Build, train, and evaluate ML models using services such as AWS SageMaker, Bedrock, Glue, Athena, Redshift, and RDS. Help create the knowledge artefacts for Data Scientist around DevOps and ML Ops. Where required, hand hold the data scientist and assist them with DevOps engineering issues, package installation issues, creating a Docker container, ML Ops tooling issues. Develop and expose secure APIs using Apigee, enabling easy access to AI functionality across the organization. Manage the entire ML life cycle-from training and validation to versioning, deployment, monitoring, and governance. Build automation pipelines and CI/CD integrations for ML projects using tools like Jenkins and Maven. Solve common challenges faced by Data Scientists, such as model reproducibility, deployment portability, and environment standardization. Assist the product owner to define and implement the ML Ops roadmap. Support knowledge sharing and mentorship across data Scientists teams, promoting a best practice-first culture. What skills are required? Minimum skills: Degree in computer science, economics, data science or another technical field (eg maths, physics, statistics etc.), or equivalent relevant experience Strong programming proficiency in Python (or R), with practical experience in machine learning and statistical modelling. Proven experience delivering end-to-end data science products, including both experimentation and deployment. Solid understanding of data cleaning, feature engineering, and model performance evaluation. Essential skills: Demonstrated experience deploying and maintaining AI/ML models in production environments. Hands-on experience with AWS Machine Learning and Data services: SageMaker, Bedrock, Glue, Kendra, Lambda, ECS Fargate, and Redshift. Familiarity with deploying Hugging Face models (eg, NLP, vision, and generative models) within AWS environments. Ability to develop and host microservices and REST APIs using Flask, FastAPI, or equivalent frameworks. Proficiency with SQL, version control (Git), and working with Jupyter or RStudio environments. Experience integrating with CI/CD pipelines and infrastructure tools like Jenkins, Maven, and Chef. Strong cross-functional collaboration skills and the ability to explain technical concepts to non technical stakeholders. Ability to work across cloud-based architectures. Tools & Technologies: AWS Services: SageMaker, Bedrock, Glue, ECS Fargate, Athena, Kendra, RDS, Redshift, Lambda, CloudWatch Other Tooling: Apigee, Hugging Face, RStudio, Jupyter, Git, Jenkins, Linux Languages & Frameworks: Python, R, Flask, FastAPI, SQL
