Hire Machine Learning Developers
Hire ML developers for models that must survive new data.
RaftLabs provides machine learning developers for teams that need data audits, feature pipelines, model training, validation, serving, monitoring, or retraining inside an existing product. The role is matched to one decision and its labels, baseline, release authority, and operating owner.
Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.
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The brief
Start with what is not working.
Good software decisions begin with the constraint, not a list of features or a preferred technology.
Does a notebook model perform well on a static sample but lack a dependable path into the product?
Can the team explain where labels came from, what baseline the model beats, and who responds when data changes?
Plain answer
Hiring machine learning developers adds capacity for data preparation, feature pipelines, model training, validation, serving, monitoring, and retraining. RaftLabs matches developers to one decision, its labels, baseline, and production owner. Rates are published per person monthly, with an initial four-week term.
The model worked in the notebook. The labels arrived two months late.
Backtesting looked strong because the data included information the product would not know at decision time. Serving the model exposed the gap. The production score was solving a different problem from the experiment.
An embedded ML developer should connect data, validation, serving, and product use around one decision. The model is only one part of that path.
Relevant ML delivery proof
- less clinical decision time in a patient-monitoring AI project
- 20%
- Project-recorded outcome, not independently audited
- patients reached during the documented 12-week release
- 150+
- RaftLabs project record
- clinics recorded in the same project period
- 80+
- RaftLabs project record
The patient-monitoring AI case used historical baselines and device readings to support clinical prioritisation. Its figures belong to that product, and the page records its evidence boundary. They do not promise the result of a different model, data set, or embedded staffing term.
Embedded ML capacity fits when the client owns the product decision and needs sustained work across data, model, and operation.
Choose a fixed project for one stable outcome, or start with a data audit when the labels and baseline are still uncertain.
The team can name the prediction, user, decision time, current method, and cost of errors.
Historical inputs and outcomes can be linked with timestamps and stable identities.
A technical and domain owner can review the model, threshold, release, and monitoring.
The request begins with a model type but no business decision.
Historical outcomes are missing and nobody can create or review a useful label.
The team expects a developer to certify a high-impact decision without domain, legal, or risk ownership.
Ownership
What an embedded ML developer can take on
Data and label audit
Trace where inputs and outcomes come from, reconstruct what was known at decision time, inspect missingness and imbalance, and expose leakage before a model inherits it.
Features, models, and baselines
Build reproducible transformations and compare simple rules or statistical baselines with candidate models. Complexity is justified only when it improves the agreed decision on held-out data.
Serving and product integration
Package inference with versioned inputs and outputs, latency and resource limits, fallbacks, access controls, and a product path that shows enough context for the user to act.
Monitoring and retraining
Track data freshness, drift, score distribution, delayed outcomes, segment performance, and model version. Retraining follows an approved trigger and comparison rather than an automatic calendar.
Should you hire an ML developer or commission an ML project?
Embedded ML developer vs fixed ML project
| Embedded developer | Fixed project | |
|---|---|---|
| Best fit | An evolving model backlog inside an established team | One prediction with stable acceptance criteria |
| Direction | Client owns daily priorities and release decisions | Delivery team owns the agreed project plan |
| Data uncertainty | Handled across the ongoing backlog | Bounded through audit or feasibility phase |
| Commercial model | Monthly per-person rate | Fixed price for the agreed phase |
| Alternative | AI engineer for LLM, RAG, or agent systems | Data engineering when the pipeline is the main gap |
Hiring one person will not solve a missing decision owner or unlabelled history. Those are the first constraints to surface.
Onboarding
From model backlog to an owned production decision
The first four weeks test data access, validation discipline, product contribution, and handover.
- Step 0101
Define the decision and baseline
Choose one prediction, its users, decision time, labels, current method, error costs, and release threshold. Keep domain and high-impact authority with the client.
- Step 0202
Match data and engineering depth
Review data quality, modelling, serving, infrastructure, and domain needs. Prepare access and a bounded first task that exposes the full path without risking a critical decision.
- Step 0303
Build through validation
Work in the client's process while testing leakage, bias, calibration, latency, cost, failure handling, and product integration. Version data assumptions, features, models, and results.
- Step 0404
Review and hand over
Assess held-out and operating evidence, update data and model runbooks, and review the next-term need. Continue, change the boundary, adjust the role, or close cleanly.
Where embedded ML engagements fail
- The random split leaks the future
- When behaviour changes over time, validate on later periods and reconstruct only the information available before each decision. A shuffled split hides the decay that time-based validation exposes.
- Accuracy hides the costly error
- Choose measures and thresholds around the business cost of false positives, false negatives, delay, and review capacity. A model can be mostly accurate and still lose money on the cases it gets wrong.
- The model has no product owner
- Someone must decide how a score changes work, what explanation users need, and when a person can override it. Without that owner, the model optimises a metric nobody acts on.
- Retraining silently changes policy
- Compare every candidate with the current version, record the result, and require approval before a new model changes production decisions. A retrain is a release, not a refresh.
Related hiring options
Choose the role or delivery model
- 01
Hire AI engineers
Hire for LLM, RAG, agent, document, image, or voice product systems and their evaluations. The hire covers the full application path, not just the model.
- 02
Machine learning development
Choose a fixed project for one prediction, data boundary, validation plan, and production release.
- 03
Data engineering
Start with ingestion, modelling, quality, lineage, and operating data when the pipeline is the main constraint.
Work with us
Show us the model or data decision that lacks a production owner.
Bring the decision, historical inputs, labels, current baseline, and product boundary. We will recommend an ML developer, a team, or a fixed project.
- Scope and cost agreed before work starts. No surprises. No obligation.
- Working prototype within 3 weeks of kickoff.
- Pay by milestone. You see progress before each invoice.
- 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
- All conversations are NDA-protected.
Common questions
Choose one production decision or one platform boundary: data and feature preparation, a forecasting or classification model, serving, monitoring, or retraining. The developer can work across the path, but a named decision and baseline keep research, infrastructure, and product work connected to one measurable result.
An ML developer is the clearer fit when the central work is labelled data, features, model training, statistical validation, serving, drift, and retraining. An AI engineer often focuses on foundation-model products, retrieval, prompts, tools, evaluations, and application integration. The role should follow the actual backlog rather than the fashionable title.
Prepare representative historical inputs, outcome labels, timestamps, stable entity identifiers, data definitions, access rules, and the current decision baseline. The first term can audit and repair these foundations. A large table is not automatically usable if labels are delayed, missing, inconsistent, or created after the decision time.
Compare it with the current decision on held-out time periods and measures tied to the business error. Inspect leakage, calibration, important segments, false positives and negatives, latency, and failure behaviour. The release owner should approve a threshold and rollback path; an aggregate accuracy score is rarely enough.
Hire a data scientist when the uncertainty is analytical feasibility or discovering patterns; hire an ML engineer when a validated use case needs reliable deployment, integration, monitoring, and lifecycle ownership. In practice: data scientist does analysis, reports, munging, SQL; ML engineer builds software, pipelines, products that predict. The decision-first frame: start with the decision that needs to move. Define the workflow, identify the data, establish the controls, set the outcome metric, then hire. The model worked in the notebook. The labels arrived two months late. That is the data reality.
The provenance-question test. The candidate who asks about the data's provenance in the first five minutes is the one who has been burned by a production model. The commodity frame. ML is becoming a commodity; data work is the hard part: building, cleaning, and validating data. Software engineering skills matter more than ML skills; most problems do not need a great model, they need good data pipelines. The underspecified-brief work sample. Small messy dataset, deliberately vague brief, two to three hours: judge judgment, not accuracy. Reuse a real anonymized problem the team already solved. No PhD required. Practical ML engineering and production experience over academic credentials.
Options: direct hire, contract, contract-to-hire, nearshore, offshore, hourly, project-based. Matching can happen in days via agencies versus weeks for a direct hire. Hourly suits ongoing support and small tasks; project-based suits well-defined objectives like building or optimizing a model. Assess production ownership via specific deployed models, MLOps practices, monitoring strategies, and how they handled model failures. Look for full-lifecycle understanding, not just training. The credential-filter warning: a credential filter shrinks a scarce pool without improving it.
Rates are published per person monthly, consistent with RaftLabs' dedicated-team model. A lean team starts with one developer or a small group. Role mix, allocation, duration, data access, and specialist depth set the final rate before the initial term.