our expertise
Your trusted Data & AI talent
talent
partner.
partner.
We help enterprises build and scale high-performing Data & AI teams.
our expertise
Your trusted Data & AI talent
talent
partner.
partner.
We help enterprises build and scale high-performing Data & AI teams.
our expertise
Your trusted Data & AI talent
talent
partner.
partner.
We help enterprises build and scale high-performing Data & AI teams.
Data Engineering
We deliver architects and engineers who build enterprise-grade data platforms. From pipelines to modeling, our talent keeps your data foundation strong and future-ready.
Typical roles: Data Architects, Senior Data Engineers, Platform Engineers
Typical initiatives: building or modernising cloud data platforms, warehouses and lakehouses
Typical outcomes: cleaner pipelines, faster delivery and fewer issues in production
35%
Reduction in time to onboard new data sources into the analytics platform.
Data Engineering
We deliver architects and engineers who build enterprise-grade data platforms. From pipelines to modeling, our talent keeps your data foundation strong and future-ready.
Typical roles: Data Architects, Senior Data Engineers, Platform Engineers
Typical initiatives: building or modernising cloud data platforms, warehouses and lakehouses
Typical outcomes: cleaner pipelines, faster delivery and fewer issues in production
35%
Reduction in time to onboard new data sources into the analytics platform.
Data Engineering
We deliver architects and engineers who build enterprise-grade data platforms. From pipelines to modeling, our talent keeps your data foundation strong and future-ready.
Typical roles: Data Architects, Senior Data Engineers, Platform Engineers
Typical initiatives: building or modernising cloud data platforms, warehouses and lakehouses
Typical outcomes: cleaner pipelines, faster delivery and fewer issues in production
35%
Reduction in time to onboard new data sources into the analytics platform.
ML & LLM Ops
Specialists in ML pipelines, orchestration, model lifecycle management, and LLM deployment. Talent that brings reliability, observability, and scale to your AI systems.
Typical roles: ML Engineers, MLOps Engineers, LLM Ops Specialists
Typical initiatives: deploying and monitoring models, building inference pipelines, integrating LLMs into products
Typical outcomes: more reliable deployments, faster iteration, reduced incidents
40%
Reduction in time needed to take new models from prototype to production.
ML & LLM Ops
Specialists in ML pipelines, orchestration, model lifecycle management, and LLM deployment. Talent that brings reliability, observability, and scale to your AI systems.
Typical roles: ML Engineers, MLOps Engineers, LLM Ops Specialists
Typical initiatives: deploying and monitoring models, building inference pipelines, integrating LLMs into products
Typical outcomes: more reliable deployments, faster iteration, reduced incidents
40%
Reduction in time needed to take new models from prototype to production.
ML & LLM Ops
Specialists in ML pipelines, orchestration, model lifecycle management, and LLM deployment. Talent that brings reliability, observability, and scale to your AI systems.
Typical roles: ML Engineers, MLOps Engineers, LLM Ops Specialists
Typical initiatives: deploying and monitoring models, building inference pipelines, integrating LLMs into products
Typical outcomes: more reliable deployments, faster iteration, reduced incidents
40%
Reduction in time needed to take new models from prototype to production.
Applied AI
Engineers who turn AI into production value — from automation workflows to applied model integration. Enterprise-ready talent that ships real outcomes, not prototypes.
Typical roles: Applied AI Engineers, AI Product Engineers, Solution Engineers
Typical initiatives: building AI-powered features, internal copilots and automation workflows
Typical outcomes: higher adoption of AI tools, visible time savings for end users
20%
Reduction in average handling time for a key internal workflow.
Applied AI
Engineers who turn AI into production value — from automation workflows to applied model integration. Enterprise-ready talent that ships real outcomes, not prototypes.
Typical roles: Applied AI Engineers, AI Product Engineers, Solution Engineers
Typical initiatives: building AI-powered features, internal copilots and automation workflows
Typical outcomes: higher adoption of AI tools, visible time savings for end users
20%
Reduction in average handling time for a key internal workflow.
Applied AI
Engineers who turn AI into production value — from automation workflows to applied model integration. Enterprise-ready talent that ships real outcomes, not prototypes.
Typical roles: Applied AI Engineers, AI Product Engineers, Solution Engineers
Typical initiatives: building AI-powered features, internal copilots and automation workflows
Typical outcomes: higher adoption of AI tools, visible time savings for end users
20%
Reduction in average handling time for a key internal workflow.
Analytics, Insights & BI
Analytics engineers and BI experts who translate data into clear decisions. Fast reporting, strong modeling, and insights built to support leadership and operations.
Typical roles: Analytics Engineers, BI Developers, Data Analysts
Typical initiatives: building semantic layers, dashboards and self-service reporting
Typical outcomes: better visibility, faster decision cycles, less manual reporting work
25%
Decrease in manual reporting effort across business units.
Analytics, Insights & BI
Analytics engineers and BI experts who translate data into clear decisions. Fast reporting, strong modeling, and insights built to support leadership and operations.
Typical roles: Analytics Engineers, BI Developers, Data Analysts
Typical initiatives: building semantic layers, dashboards and self-service reporting
Typical outcomes: better visibility, faster decision cycles, less manual reporting work
25%
Decrease in manual reporting effort across business units.
Analytics, Insights & BI
Analytics engineers and BI experts who translate data into clear decisions. Fast reporting, strong modeling, and insights built to support leadership and operations.
Typical roles: Analytics Engineers, BI Developers, Data Analysts
Typical initiatives: building semantic layers, dashboards and self-service reporting
Typical outcomes: better visibility, faster decision cycles, less manual reporting work
25%
Decrease in manual reporting effort across business units.
Data Governance
Governance leads and quality specialists who put structure around data, from ownership and definitions to quality rules and controls, so information is always trusted.
Typical roles: Data Governance Leads, Data Stewards, Data Quality Specialists
Typical initiatives: policies, data catalogues, quality frameworks and control processes
Typical outcomes: fewer data issues, better compliance posture, smoother audits
50%
Reduction in critical data quality issues on key regulatory reports.
Data Governance
Governance leads and quality specialists who put structure around data, from ownership and definitions to quality rules and controls, so information is always trusted.
Typical roles: Data Governance Leads, Data Stewards, Data Quality Specialists
Typical initiatives: policies, data catalogues, quality frameworks and control processes
Typical outcomes: fewer data issues, better compliance posture, smoother audits
50%
Reduction in critical data quality issues on key regulatory reports.
Data Governance
Governance leads and quality specialists who put structure around data, from ownership and definitions to quality rules and controls, so information is always trusted.
Typical roles: Data Governance Leads, Data Stewards, Data Quality Specialists
Typical initiatives: policies, data catalogues, quality frameworks and control processes
Typical outcomes: fewer data issues, better compliance posture, smoother audits
50%
Reduction in critical data quality issues on key regulatory reports.
Program & Change Delivery
Program managers and delivery leads who drive complex Data & AI initiatives. Structured execution, stakeholder alignment, and measurable impact from start to finish.
Typical roles: Program Managers, Change Management Specialists, Scrum Masters
Typical initiatives: standing up data and AI programmes, coordinating multi-vendor delivery
Typical outcomes: clearer governance, on-time milestones, fewer surprises in production
2x
Increase in number of Data & AI projects delivered on schedule.
96%
Decrease in erroneous false-positives in large data sets.
62%
Decrease in erroneous false-positives in large data sets.
Program & Change Delivery
Program managers and delivery leads who drive complex Data & AI initiatives. Structured execution, stakeholder alignment, and measurable impact from start to finish.
Typical roles: Program Managers, Change Management Specialists, Scrum Masters
Typical initiatives: standing up data and AI programmes, coordinating multi-vendor delivery
Typical outcomes: clearer governance, on-time milestones, fewer surprises in production
2x
Increase in number of Data & AI projects delivered on schedule.
96%
Decrease in erroneous false-positives in large data sets.
62%
Decrease in erroneous false-positives in large data sets.
Program & Change Delivery
Program managers and delivery leads who drive complex Data & AI initiatives. Structured execution, stakeholder alignment, and measurable impact from start to finish.
Typical roles: Program Managers, Change Management Specialists, Scrum Masters
Typical initiatives: standing up data and AI programmes, coordinating multi-vendor delivery
Typical outcomes: clearer governance, on-time milestones, fewer surprises in production
2x
Increase in number of Data & AI projects delivered on schedule.
96%
Decrease in erroneous false-positives in large data sets.
62%
Decrease in erroneous false-positives in large data sets.
"What stood out with Calimala was the mix of experience and tooling. They understood what we needed from day one, and their platform gave our stakeholders confidence that every shortlist was well thought through."
Joseph
Director, Data Analytics
"What stood out with Calimala was the mix of experience and tooling. They understood what we needed from day one, and their platform gave our stakeholders confidence that every shortlist was well thought through."
Joseph
Director, Data Analytics
"What stood out with Calimala was the mix of experience and tooling. They understood what we needed from day one, and their platform gave our stakeholders confidence that every shortlist was well thought through."
Joseph
Director, Data Analytics