Free resource for GCC talent teamsUpdated March 2026
GCC AI roles guide
15 AI/ML roles explained for talent teams, with salary benchmarks, skill breakdowns, hiring timelines and the mistakes to avoid.
Time to hire
Open market time to hire · indicative60 to 90 daysWith Viamagus select · time to verdicts3 to 5 written verdicts in 48 hoursThis role is on our bench. Start dates follow each candidate’s notice period.4 to 10 years experience
What they do
Builds end-to-end ML pipelines, works with data engineers on feature stores, owns model performance in production, and collaborates with product teams to translate business problems into ML solutions.
Key skills
Where to find them
Common hiring mistakes
- Testing coding only: not system design or model lifecycle thinking
- Ignoring MLOps skills: deployment matters as much as model accuracy
- Not asking about scale: how large a dataset have they worked on?
Time to hire
Open market time to hire · indicative45 to 75 daysWith Viamagus select · time to verdicts3 to 5 written verdicts in 48 hoursThis role is on our bench. Start dates follow each candidate’s notice period.3 to 15 years experience
What they do
Defines metrics, runs experiments, builds predictive models, and translates statistical findings into business recommendations. Often the analytical conscience of a product or business team.
Key skills
Where to find them
Common hiring mistakes
- Asking only about tools: not about how they communicate findings to business
- Skipping domain fit: a BFSI data scientist thinks very differently from a retail one
- Not checking SQL depth: most production DS roles need strong query skills
Time to hire
Open market time to hire · indicative75 to 100 daysWith Viamagus select · time to verdicts3 to 5 written verdicts in 48 hoursThis role is on our bench. Start dates follow each candidate’s notice period.2 to 8 years experience
What they do
Designs and builds LLM-powered systems: chatbots, document Q&A, agentic workflows. Works across the full stack from prompt design to evaluation frameworks and production deployment.
Key skills
Where to find them
Common hiring mistakes
- Hiring for prompt writing only: real value is in system design around LLMs
- Not testing RAG architecture: critical for most enterprise GenAI use cases
- Ignoring cost awareness: optimising API costs is a key skill
Time to hire
Open market time to hire · indicative60 to 90 daysWith Viamagus select · time to verdicts3 to 5 written verdicts in 48 hoursThis role is on our bench. Start dates follow each candidate’s notice period.3 to 9 years experience
What they do
Builds text pipelines for classification, extraction, summarisation, and generation. Common in BFSI for regulatory document processing and in healthcare for clinical notes.
Key skills
Where to find them
Common hiring mistakes
- Confusing NLP engineers with data scientists: NLP is a deep specialisation
- Not asking about fine-tuning vs. API-based approaches
- Skipping evaluation methodology: precision/recall tradeoffs matter in production NLP
Time to hire
Open market time to hire · indicative60 to 90 daysWith Viamagus select · time to verdicts3 to 5 written verdicts in 48 hoursThis role is on our bench. Start dates follow each candidate’s notice period.3 to 9 years experience
What they do
Develops models that interpret images and video streams. Common in manufacturing (defect detection), healthcare (diagnostics), and retail (shelf analytics).
Key skills
Where to find them
Common hiring mistakes
- Not testing for edge deployment: many CV roles run on embedded devices, not cloud
- Ignoring domain specifics: medical imaging is very different from retail CV
- Skipping performance optimisation skills: latency matters in real-time CV
Time to hire
Open market time to hire · indicative90 to 120 daysWith Viamagus selectScoped with you firstNot on our bench. An engineer scopes the role with you and confirms the timing first. For AI leadership, ask about a fractional AI leader.8 to 15 years experience
What they do
Owns the technical vision for AI systems. Decides what gets built vs. bought, designs the data and model lifecycle, and ensures the AI platform scales with the business.
Key skills
Where to find them
Common hiring mistakes
- Testing them on model building: architects should be evaluated on design decisions, not code
- Not exploring vendor-neutral thinking: good architects know when not to use AI
- Missing soft skills: they need to influence engineering teams and explain to business
Time to hire
Open market time to hire · indicative45 to 70 daysWith Viamagus selectScoped with you firstNot on our bench. An engineer scopes the role with you and confirms the timing first.3 to 10 years experience
What they do
Designs data pipelines, manages data warehouses, ensures data quality, and makes data available reliably for analytics and ML workloads.
Key skills
Where to find them
Common hiring mistakes
- Not testing for data quality thinking: pipelines that produce bad data are worse than no pipeline
- Skipping orchestration skills: Airflow/dbt separates good from great
- Ignoring cost optimisation: cloud data costs spiral without an engineer who cares
Time to hire
Open market time to hire · indicative90 to 120 daysWith Viamagus selectScoped with you firstNot on our bench. An engineer scopes the role with you and confirms the timing first.8 to 15 years experience
What they do
Makes the strategic data decisions: what gets stored where, how it is governed, how different teams access it, and how the data platform scales with the business.
Key skills
Where to find them
Common hiring mistakes
- Hiring a senior data engineer for an architecture role: different mindset
- Not probing governance experience: data contracts and quality SLAs matter at scale
- Skipping business alignment: a good data architect speaks to finance and ops, not just tech
Time to hire
Open market time to hire · indicative40 to 60 daysWith Viamagus selectScoped with you firstNot on our bench. An engineer scopes the role with you and confirms the timing first.3 to 8 years experience
What they do
Builds the analytics layer: clean, versioned, tested SQL models that turn raw tables into business-ready datasets. The glue between engineering and business intelligence.
Key skills
Where to find them
Common hiring mistakes
- Confusing them with BI developers: analytics engineers build the data models BI developers use
- Not testing dbt skills: it is the defining tool of this role in 2026
- Skipping data modelling theory: star schema and dimensional modelling still matter
Time to hire
Open market time to hire · indicative60 to 90 daysWith Viamagus select · time to verdicts3 to 5 written verdicts in 48 hoursThis role is on our bench. Start dates follow each candidate’s notice period.4 to 10 years experience
What they do
Owns the ML platform: experiment tracking, model registry, feature stores, training infrastructure, and deployment pipelines. Makes ML engineers faster and more reliable.
Key skills
Where to find them
Common hiring mistakes
- Treating it like a DevOps role: ML platform engineers need to understand model lifecycle deeply
- Not asking about developer experience: best platform engineers obsess over internal tooling UX
- Ignoring compute cost management: GPU budgets are significant
Time to hire
Open market time to hire · indicative50 to 80 daysWith Viamagus select · time to verdicts3 to 5 written verdicts in 48 hoursThis role is on our bench. Start dates follow each candidate’s notice period.3 to 9 years experience
What they do
Owns the model deployment lifecycle: CI/CD for ML, monitoring for model degradation, retraining triggers, and incident response when models behave unexpectedly in production.
Key skills
Where to find them
Common hiring mistakes
- Hiring a DevOps engineer and calling them MLOps: they need ML fluency, not just infrastructure skills
- Not testing model monitoring knowledge: drift detection and alerting are often neglected
- Ignoring retraining pipeline design: production models need refresh strategies
Time to hire
Open market time to hire · indicative60 to 90 daysWith Viamagus selectScoped with you firstNot on our bench. An engineer scopes the role with you and confirms the timing first.4 to 10 years experience
What they do
Defines what AI gets built and why. Works with data scientists, engineers, and business stakeholders to prioritise AI use cases, own the product roadmap, and measure AI product success.
Key skills
Where to find them
Common hiring mistakes
- Hiring a generic PM: AI PMs need to understand model tradeoffs, data dependencies, and AI failure modes
- Not testing for ambiguity tolerance: AI products are inherently probabilistic
- Skipping technical depth: they need to reason about ML systems even without coding
Time to hire
Open market time to hire · indicative120 to 180 daysWith Viamagus selectScoped with you firstNot on our bench. An engineer scopes the role with you and confirms the timing first. For AI leadership, ask about a fractional AI leader.12 to 20+ years experience
What they do
Owns AI at the organisational level: defines what to build, builds the team, manages AI risk, and communicates AI value to global leadership and the board.
Key skills
Where to find them
Common hiring mistakes
- Assessing technical skills like a senior engineer: strategy and influence matter more at this level
- Not probing for GCC operating model fit: global-local tension must be navigated
- Skipping AI governance experience: responsible AI is non-negotiable in regulated sectors
Time to hire
Open market time to hire · indicative90 to 120 daysWith Viamagus selectScoped with you firstNot on our bench. An engineer scopes the role with you and confirms the timing first. For AI leadership, ask about a fractional AI leader.8 to 15 years experience
What they do
Manages data scientists, owns the function roadmap, ensures data science outputs are operationalised, and is the face of data science to business leadership.
Key skills
Where to find them
Common hiring mistakes
- Evaluating as a senior data scientist: leadership and prioritisation skills are primary criteria
- Not exploring how they handle underperformance: critical for a function lead
- Skipping business translation skills: the best heads of DS make complex models legible to non-technical leaders
Time to hire
Open market time to hire · indicative90 to 150 daysWith Viamagus selectScoped with you firstNot on our bench. An engineer scopes the role with you and confirms the timing first. For AI leadership, ask about a fractional AI leader.10 to 18 years experience
What they do
Builds the AI CoE: establishes use case prioritisation frameworks, AI governance policies, enablement programmes for non-AI teams, and tracks AI ROI across the organisation.
Key skills
Where to find them
Common hiring mistakes
- Hiring a technical AI lead for a change management role: different skills
- Not exploring cross-functional influence: CoE leads work across business units without direct authority
- Skipping governance framework experience: essential for regulated industries
Hiring for one of these roles?
Several of these roles are on our bench: AI / ML, data science, AI infrastructure and MLOps. For those, you get 3 to 5 written verdicts within 48 hours. For AI leadership roles, ask about fractional AI leadership.