Senior ML engineers for production forecasting and MLOps. We connect models to reliable pipelines, evaluation, and monitoring so your team can use them with confidence.
Models stay in your repositories, with evaluation gates before production handover.
Bring a forecasting model into production, or establish the foundations that make every model easier to reproduce and operate.
Today: A promising forecast is still stuck in a notebook.
Build a scheduled forecasting service with reproducible inputs, automated retraining, and evaluation gates. Monitor data changes and performance so the team knows when to investigate.
First useful milestone One model in production with automated retraining and monitoring wired end to end.
Today: Every model has its own route to production.
Connect experiment tracking, a model registry, and CI for models. Give your team a reproducible train-evaluate-register workflow and a controlled way to promote approved versions.
First useful milestone Tracked experiments and a repeatable train-evaluate-register flow behind an evaluation gate.
Scope and timing are agreed after discovery.
Clear outputs your business can evaluate and your technical team can operate. Delivered in your repositories.
A trained, versioned model with ownership notes and a deployment path your team approves. The artifact, evaluation record, and operating context travel together.
In your repository: A versioned model, Its evaluation record, A deployment path.
In your repositories, owned by your team.
The business objective, target metric, input data, baseline, and acceptance criteria in one readable document.
Model briefDrift, quality, and performance checks with clear escalation rules and an owner for the response.
Monitoring planRetraining cadence, model assumptions, known limitations, and maintenance guidance, walked through together.
Handover documentationAnonymized engagements, each with the team we placed, how long it ran and what it changed.
Agree how the model will help the business make a decision, then build the workflow your team needs to operate it.
Agree the business objective, the metric that means success, and the acceptance criteria. Make the baseline and constraints explicit before modeling starts.
Result A model brief and evaluation specification
Build a clean feature pipeline and a reproducible baseline. For forecasting, test on later periods that were not available during training and compare like-for-like horizons.
Result A baseline, features, and an evaluation record
Version the model, automate training and evaluation, and serve predictions through an API or batch job. Make the promotion criteria and recovery path part of the workflow.
Result A production model with retraining and evaluation gates
Monitor inputs and performance, investigate changes, and evaluate retrained candidates before promotion. Walk your team through assumptions, limitations, and operating responsibilities.
Result Monitoring, a model card, and operating runbooks
You meet and interview the engineers. We agree the problem, access, evaluation criteria, and operating responsibilities before work starts.
Meet the companyYes. We can start with an existing notebook or model, check the inputs and evaluation method, and work on the pipeline, serving path, and monitoring it needs for production.
For forecasting, the first useful milestone is one model in production with automated retraining and monitoring. For MLOps, it is tracked experiments and a repeatable train-evaluate-register workflow behind an evaluation gate. Scope and timing are agreed after discovery.
An indicative plan allows one week for framing and one to two weeks for baseline and data work. Productionisation continues iteratively, with a broader operating handover from month four. Data readiness, dependencies, and scope determine the actual schedule.
A change in input data is a reason to investigate, not proof that a model has stopped working. We review performance when outcomes become available, then evaluate any retrained candidate against the agreed criteria before promoting it.
Meet your specialists.
Select a person to explore their experience.
Showing Luan, ML Engineer
Data science & AI
ML Engineer
8 yearsforecasting and MLOps
ML Engineer
Data science & AI
Data Scientist
7 yearsdemand forecasting and causal
Data Scientist
You interview and approve every engineer. We review technical experience, working style, and language fit.
Typically 1-2 senior engineers, with an indicative rate of €290-450 / day per engineer. Shortlist target: 5 business days from an agreed brief.
Indicative sequence. We confirm timing, capacity, and terms for your role.
Sample profiles from our network. Availability, rates, and fit confirmed for your brief. Language levels use CEFR; full CVs and interview details accompany your shortlist.
Connect model development to the workflow that uses its predictions. We work with your existing tools and choose the missing pieces around the job.
Talk through your stackWe work with your existing stack and choose the tools around the job. These are the ones we use most in this area.
Build and compare models against an agreed baseline.
Model demand over time and understand uncertainty.
Track experiments, version artifacts, and make model runs reproducible.
Train and run models on infrastructure suited to your workloads.
Deliver predictions through an API or a scheduled batch job.
Observe data changes and model performance, with a clear response.
Tell us what the model needs to help you decide and where the work is stuck. We’ll agree a useful starting point together.
