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Our client, a growing IT Consulting firm specializing in the Federal sector, are hiring a Senior ServiceNow Developer  to join the team remotely on a contract basis. The successful candidate will help implement ServiceNow Public Sector Digital Services (PSDS) for a U.S. Department of Defense customer, building case management workflows, self-service portals and system integrations to support the program's day-to-day operations.


Responsibilities

• Develop, test and deploy ServiceNow applications and workflows using JavaScript, Glide APIs, Business Rules, Client Scripts, Script Includes and UI Policies.

• Configure case management in CSM and PSDS, covering intake, routing, assignment, approvals, service-level agreements, escalations, communications and resolution.

• Extend ServiceNow data models and build portals, forms and record producers that let users submit requests, track their cases and find answers in the knowledge base.

• Automate case workflows with Flow Designer and connect ServiceNow to external systems using IntegrationHub, REST and SOAP APIs and Scripted REST APIs.

• Configure Agent Workspace or Configurable Workspace and create reports, dashboards and notifications to help teams manage caseloads and monitor service performance.

• Configure roles, groups and access controls to protect sensitive case data and work with security teams to meet applicable DoD and federal requirements.

• Work with architects, business analysts, product owners and subject matter experts to turn requirements into technical designs.

• Review code, support testing and releases, troubleshoot issues across development, test, production and document platform configurations and integrations.

Skillset

• U.S. citizenship, an active DoD security clearance and the ability to work in a secure federal environment are required.

• Bachelor's degree in Computer Science, Information Technology, Engineering or a related field.

• At least 7 years of hands-on ServiceNow development and implementation experience across the full development lifecycle, from requirements and technical design through deployment and production support.

• Experience developing ServiceNow CSM or PSDS solutions, with a strong understanding of enterprise case management. Direct PSDS implementation or advanced CSM delivery for government or defense customers is highly desirable.

• Strong JavaScript and Glide API skills, including client- and server-side scripting, custom application development, and ServiceNow data modeling.

• Hands-on experience with Flow Designer, IntegrationHub, REST and SOAP APIs, and enterprise integrations. MID Server experience is a plus..

• Experience configuring ServiceNow Workspace, roles, groups, access control lists and data access rules. Experience with portals, digital intake or knowledge management is preferred.

• Familiarity with DoD STIGs, the Risk Management Framework (RMF), NIST standards or Section 508 accessibility requirements is a plus.

• ServiceNow certifications such as CSA, CAD or CIS-CSM are a bonus.

• Experience with App Engine Studio, scoped applications, Automated Test Framework (ATF), source control, or ServiceNow DevOps practices.

Our client, a global IT Consulting organisation, is hiring an Enterprise AI Architect to join their Dublin, Ireland on a contract basis. The successful candidate will define the target architecture for the organisation's Enterprise AI Control Plane, setting out the capabilities needed to govern AI, who will own them, and how information will flow between teams and systems.


Responsibilities

• Review the organisation's existing architecture and AI governance capabilities to identify gaps, constraints and dependencies.

• Define the target architecture for the Enterprise AI Control Plane and build a capability model that describes what each capability does, who owns it and how it relates to others.

• Turn business, governance, risk, and operational needs into architecture requirements and document how those requirements shape design decisions.

• Develop a conceptual information model showing how AI assets, ownership, risks, controls, policies and evidence relate throughout the AI lifecycle.

• Establish architecture principles and guardrails that guide detailed design and give delivery teams flexibility in their technology choices.

• Help prioritise capabilities for the first release, define how the architecture will evolve across later phases and contribute to the implementation roadmap.

• Lead architecture workshops and work with business, governance and technology teams to keep detailed designs aligned with the target architecture.

Skillset

• Strong enterprise architecture experience in complex or regulated organisations.

• Experience assessing existing architectures, defining target states and planning the transition between them.

• A good understanding of enterprise AI architecture, AI governance and how organisations manage AI assets throughout their lifecycle.

• Experience developing enterprise capability models and using business requirements to shape logical architectures and conceptual information models.

• An understanding of the governance, risk, security, privacy and regulatory considerations that influence enterprise AI architecture.

• Ability to define capability boundaries, map dependencies, and set principles and criteria that guide design decisions.

• Strong workshop facilitation and stakeholder engagement skills, with the ability to explain architecture decisions and trade-offs to senior leaders.

Our client, a global IT Consulting organisation, is hiring an AI Platform Architect to join the team in Dublin, Ireland on a contract basis. The successful candidate will translate the target architecture into designs that engineering teams can build from, specifying how the organisation will register AI assets, enforce policies and collect evidence across its platforms.


Responsibilities

• Review existing platforms and controls, and establish which systems hold the authoritative records for AI assets, models, identities, policies, approvals, incidents, costs and lifecycle status.

• Design integrations using APIs, events, connectors, SDKs and workflows.

• Define who owns each interface, how it is secured and monitored, how data discrepancies are resolved and what happens if an integration fails.

• Decide where to enforce AI policies across model gateways and services, agent runtimes, tools, data layers and business applications.

• Define how teams will monitor those controls and capture evidence that they work.

• Evaluate how the organisation can identify and govern AI capabilities built into Microsoft 365, Salesforce, Snowflake, Databricks and other enterprise applications.

• Build and run a secure test environment using synthetic, masked or otherwise approved data.

• Configure shortlisted technologies or guide their setup, then test asset registration, risk classification, delegated actions, policy enforcement, runtime intervention and recovery.

• Document technical limitations, dependencies, workarounds and licensing implications.

• Use the findings to recommend suppliers and prioritise integrations, then hand over the designs and test evidence to internal platform teams.

Skillset

• Proven experience designing AI platforms, cloud and data architectures and integrations across enterprise systems.

• Hands-on knowledge of generative AI services, model platforms, AI agents, gateways and APIs, including how to enforce policies at runtime and monitor system behaviour.

• Experience connecting governance, identity, security, data and IT service management platforms.

• Understanding of how to manage identities for people, workloads and AI agents, including delegated permissions, privileged access, access reviews and revocation.

• Experience designing secure sandboxes, proofs of concept, or other technical validation environments, with an understanding of data residency, retention, encryption, resilience and recovery.

• Ability to assess which capabilities are available out of the box, which need configuration or custom development, and which depend on a vendor's roadmap.

• Ability to document architecture decisions clearly, demonstrate technical solutions and explain trade-offs to technical and business stakeholders.

Our client, a leading healthcare enterprise, is hiring a Portfolio Product Lead to join the team remotely on a nine-month contract. The successful candidate will lead the AI and GenAI investment roadmap, prioritising initiatives and guiding them from experimentation to adoption and measurable results.


Responsibilities

• Own the AI portfolio roadmap, balancing business value, technical feasibility, risk and delivery capacity.
• Work with business teams to identify where AI can improve processes, products or ways of working.
• Turn opportunities into clear use cases, priorities and measurable success criteria.
• Guide initiatives from discovery and experimentation through to implementation, adoption and ongoing evaluation.
• Partner with engineering, data and AI teams to assess approaches, challenge assumptions and unblock delivery.
• Align senior stakeholders on priorities, investment decisions and expected outcomes.
• Manage dependencies and risks across the portfolio, adjusting direction as evidence emerges.
• Build governance, security and human oversight into every initiative from the start.
• Track adoption and business impact, and use the results to decide what to improve, scale or stop.

Skillset

• Experience leading products, portfolios or transformation programmes in a complex organisation.
• A track record of delivering AI or GenAI initiatives into real use, with concrete examples of implementation, adoption and results.
• Current, practical exposure to AI tools and solutions, backed by a hands-on or previously hands-on technical background.
• The ability to discuss AI capabilities, data requirements, evaluation, integration and limitations with technical teams.
• Strong prioritisation skills and confidence making trade-offs between competing initiatives.
• Excellent stakeholder management, including influencing senior leaders and bringing business and technology teams together.
• A clear approach to measuring value, from setting a baseline to assessing outcomes.
• Understanding of responsible AI, governance and the challenges of introducing AI into established enterprise systems.
• Experience applying AI to software delivery, application modernisation or internal workflows.

Interested? Send your CV to kylec@alldus.com or message me for a quick conversation about the role and contract.

Our client, a global life sciences organisation, is hiring a Senior AI/ML Production Engineer to joint heir team remotely on a 12-month daily rate contract. The successful candidate will take machine learning models from prototype to production, developing the services, pipelines and infrastructure needed to run reliable, scalable AI applications in clinical research.


Responsibilities

• Turn Python prototypes and machine learning models into maintainable production services.

• Develop and optimise Python applications, REST APIs, microservices and automated data pipelines.

• Build CI/CD and MLOps pipelines for testing, deployment, model versioning, monitoring and reproducibility.

• Improve code performance, memory efficiency and reliability when processing large, complex datasets.

• Deploy AI/ML solutions on AWS and/or Azure using Docker, Kubernetes and cloud-native services.

• Implement logging, monitoring and model performance tracking, and troubleshoot production issues.

• Work with Data Science and Engineering teams to define deployment requirements and resolve technical challenges.

• Apply secure engineering practices and support relevant data privacy and regulatory requirements.

Skillset

• At least 5 years of professional software engineering experience, including deploying and supporting AI/ML solutions in production.

• Strong Python skills, with experience writing maintainable, well-tested and efficient code.

• A track record of taking machine learning models or analytical applications from prototype to production.

• Experience building APIs and backend services using FastAPI, Flask or similar frameworks.

• Practical experience with MLOps, CI/CD and automated deployment, using tools such as MLflow, Kubeflow, Airflow, DVC or Databricks.

• Experience with AWS and/or Azure, alongside Docker and Kubernetes.

• An understanding of model lifecycle management, monitoring and production troubleshooting.

• Experience with SQL databases, data pipelines and large-scale data processing.

• Familiarity with Git, automated testing, code reviews and infrastructure automation.