Initialize IT
D365 CE Functional Consultant - Hybrid/Cheshire - 6-12 months - £500 - 518 Dynamics 365 Customer Engagement (CE) Functional Consultant with strong experience across Customer Journeys, Sales, and Customer Service . Key Responsibilities (High-Level) Lead requirement gathering workshops for Customer Journeys, Customer Service (Case Management, SLAs, Queues, Knowledge Base) and Sales (Lead-Opportunity life cycle) . Analyse existing CRM processes and document functional gaps, enhancements, and solution approaches. Support knowledge transition, environment walkthroughs, and stakeholder onboarding. Review and validate high-level customizations, integrations, reporting needs, and security roles. Coordinate with technical teams for solution design, deployment, and issue resolution. Support UAT, regression testing, and release readiness activities. Ensure alignment with incident, change, and release management processes. Work closely with business users to provide functional support and process improvements. Required Skills Strong functional knowledge of D365 CE - Customer Service, Sales & Customer Journeys modules. Understanding of Plugin, Cloud flow, API. Understanding of: Case life cycle, SLAs, entitlements, queues, routing Lead, Opportunity, and basic Sales processes Customer Journeys Knowledge Base articles Ability to document business processes, requirements, and functional specifications. Ability to validate customizations, workflows, integrations, and reports at a functional level. Experience with test scenarios, UAT support, and go-live readiness. Good understanding of CRM security roles, audit controls, and compliance. Nice to Have Skills Exposure to Azure Function, integrations, and Middleware. Experience with Splunk and TIBCO ActiveMatrix BusinessWorks Plug-in.
D365 CE Functional Consultant - Hybrid/Cheshire - 6-12 months - £500 - 518 Dynamics 365 Customer Engagement (CE) Functional Consultant with strong experience across Customer Journeys, Sales, and Customer Service . Key Responsibilities (High-Level) Lead requirement gathering workshops for Customer Journeys, Customer Service (Case Management, SLAs, Queues, Knowledge Base) and Sales (Lead-Opportunity life cycle) . Analyse existing CRM processes and document functional gaps, enhancements, and solution approaches. Support knowledge transition, environment walkthroughs, and stakeholder onboarding. Review and validate high-level customizations, integrations, reporting needs, and security roles. Coordinate with technical teams for solution design, deployment, and issue resolution. Support UAT, regression testing, and release readiness activities. Ensure alignment with incident, change, and release management processes. Work closely with business users to provide functional support and process improvements. Required Skills Strong functional knowledge of D365 CE - Customer Service, Sales & Customer Journeys modules. Understanding of Plugin, Cloud flow, API. Understanding of: Case life cycle, SLAs, entitlements, queues, routing Lead, Opportunity, and basic Sales processes Customer Journeys Knowledge Base articles Ability to document business processes, requirements, and functional specifications. Ability to validate customizations, workflows, integrations, and reports at a functional level. Experience with test scenarios, UAT support, and go-live readiness. Good understanding of CRM security roles, audit controls, and compliance. Nice to Have Skills Exposure to Azure Function, integrations, and Middleware. Experience with Splunk and TIBCO ActiveMatrix BusinessWorks Plug-in.
Initialize IT
Salesforce OmniStudio Developer - remote (mostly)/UK, 6months + - up to £360 Role Description Design, build, and optimize OmniScripts, FlexCards, DataRaptors (Extract, Transform, Load), and Integration Procedures to support end-to-end Health Cloud workflows. Develop reusable, modular OmniStudio components aligned with best practices for performance, maintainability, and scalability. Implement custom Lightning Web Components (LWC) when required to enhance OmniScript UI or logic. Configure and customize Health Cloud features such as Patient 360, Assessments, Utilization Management etc. Integrate OmniScripts into Health Cloud patient/member service journeys. Build integrations involving FHIR/HL7, external healthcare systems, and REST/SOAP APIs using OmniStudio Integration Procedures. Create and maintain data mapping using DataRaptors to ensure accurate data exchange. Implement proper access control, field-level security, and data-protection measures within OmniStudio assets. Create detailed technical documentation for OmniScripts, data models, integrations, and deployment procedures. ( LLD level with in sync with HLD to be prepared by Functional Team ) Candidate must be strong expertise in:- OmniScripts FlexCards DataRaptors (ETL/Transform/Load) Integration Procedures JSON & data modelling Working knowledge of Salesforce Health Cloud, Proficiency in LWC, Apex basics, and Salesforce configuration.
Salesforce OmniStudio Developer - remote (mostly)/UK, 6months + - up to £360 Role Description Design, build, and optimize OmniScripts, FlexCards, DataRaptors (Extract, Transform, Load), and Integration Procedures to support end-to-end Health Cloud workflows. Develop reusable, modular OmniStudio components aligned with best practices for performance, maintainability, and scalability. Implement custom Lightning Web Components (LWC) when required to enhance OmniScript UI or logic. Configure and customize Health Cloud features such as Patient 360, Assessments, Utilization Management etc. Integrate OmniScripts into Health Cloud patient/member service journeys. Build integrations involving FHIR/HL7, external healthcare systems, and REST/SOAP APIs using OmniStudio Integration Procedures. Create and maintain data mapping using DataRaptors to ensure accurate data exchange. Implement proper access control, field-level security, and data-protection measures within OmniStudio assets. Create detailed technical documentation for OmniScripts, data models, integrations, and deployment procedures. ( LLD level with in sync with HLD to be prepared by Functional Team ) Candidate must be strong expertise in:- OmniScripts FlexCards DataRaptors (ETL/Transform/Load) Integration Procedures JSON & data modelling Working knowledge of Salesforce Health Cloud, Proficiency in LWC, Apex basics, and Salesforce configuration.
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
