Hire by role
Hire vetted Machine Learning Engineers who turn models into reliable services that keep performing on real-world data. Shortlist in 48 hours; working within 4 working days.
Fig. 01AI & Data
Machine Learning Engineer
AI & Data
AvailabilityShortlist in 48 h
Vetted Machine Learning Engineer profiles free to start within 4 working days of signature. Counts appear in bands (5–10, 10–20, 20+); below 5 a role shows “Shortlist in 48 h” instead of a number.
Indicative monthly rate per seat
| Level | Experience | From (USD / month) |
|---|---|---|
| Mid | 4–6 years | 5,560 |
| Senior | 6–9 years | 7,310 |
| Lead | 9+ years | 9,500 |
Quotation-based. VAT shown separately. View in SAR, AED, USD or EUR. Quotes valid for 30 days.
Includes the AI & Data practice rate of +25% on the base band.
Remote Pro Ai places vetted Machine Learning Engineers who turn models into reliable services that keep performing on real-world data. They serve models in batch, streaming or real time with A/B and shadow testing, and wire automated retraining triggers for when drift appears.
Your Machine Learning Engineer builds supervised, ranking and forecasting models with rigorous validation, and serves models at scale (batch, streaming, real-time) with A/B and shadow testing. Typical stack: Python, PyTorch, TensorFlow, scikit-learn and XGBoost.
Enterprises are moving AI budgets from pilots to systems that run every day: fraud scoring, demand forecasting, credit decisions and recommendations. These models fail quietly when data shifts, so demand is moving toward engineers who pair modelling skill with validation, monitoring and lineage, rather than notebook-only data science.
Illustrative profiles that mirror the vetted bench for this role. Names, employers, full gate scores and video introductions, where recorded, are shared after the click-through NDA.
Machine Learning Engineer
Senior7 years
Remote Pro Ai Vetted Verify credential RP-6610
Machine Learning Engineer
Lead11 years
Remote Pro Ai Vetted Verify credential RP-8229
Machine Learning Engineer
Mid5 years
Remote Pro Ai Vetted Verify credential RP-3372
Illustrative profiles that mirror the vetted bench for this role, anonymised. Names, employers, full scores and credentials are shared after the mutual NDA in the booking flow.
Builds supervised, ranking and forecasting models with rigorous validation
Engineers features and training pipelines with reproducibility and lineage
Serves models at scale (batch, streaming, real-time) with A/B and shadow testing
Monitors drift, bias and performance decay with automated retraining triggers
Collaborates with product on measurable business KPIs
Toolset on our bench
We test each capability in a live technical assessment scored by a practice lead. Nothing on this list is taken from a CV.
Mid4–6 years
Delivers well-scoped work independently within your architecture and standards.
from USD 5,560/month
Senior6–9 years
Owns a component or workstream end to end, reviews code and mentors.
from USD 7,310/month
Lead9+ years
Sets technical direction, leads the squad and reports to your leadership.
from USD 9,500/month
We never place junior profiles. A practice lead confirms every level, so the band you buy is the scope you get.
Typical certifications held by our Machine Learning Engineer bench: AWS ML Specialty · Google ML Engineer. We verify every certificate with the issuing body before a profile reaches you, and list verified credentials on each profile card.
Remote Pro Ai information security: ISO/IEC 27001 certification in progress (target Q2 2027) · SOC 2 Type II planned for 2027. Evidence and policies are in the Trust Center.
Not sure this is the right role? Describe the outcome you need and the Scope Agent maps it to roles, levels and a draft squad in minutes.
A Saudi insurer
Arabic GenAI knowledge assistant
6 engineers · 7 months
Bilingual RAG assistant with evaluation harness, human-in-the-loop review, monitoring, one-click rollback and PDPL-compliant logging
MatchIQ™ is our vetting and matching engine: 3.4% of applicants pass 5 gates. The live assessment for this role tests the 5 capabilities above against a realistic task, not trivia.
Self-serve bookings receive a shortlist within 48 hours and enterprise requests get named candidates within 72 hours. After you approve a profile and sign, your specialist is working within 4 working days, whether the request is on the bench, off-bench or on-site. When fewer than 5 Machine Learning Engineer profiles are on the bench, we shortlist from the wider vetted network in the same 48 hours.
Pricing is quotation-based and depends on level, hours and any on-site, clearance or data-residency needs. Current bands: Mid from USD 5,560/month · Senior from USD 7,310/month · Lead from USD 9,500/month. VAT is shown separately, quotes can be viewed in SAR, AED, USD or EUR, and every quotation is valid for 30 days. The Squad Builder shows your quote instantly.
Remote Pro Ai is contractually responsible for quality. Raise it at any time: a replacement starts within 4 working days; the full handover completes in under 14 days. Your first engagement also begins with a no-bill trial, one per company: 5 working days on self-serve bookings and 10 on enterprise contracts.
This role is offered at Mid, Senior and Lead level. We never place junior profiles. A practice lead confirms each specialist’s level through a live technical assessment and reference checks, so the band on your quotation reflects scope the person has already delivered rather than a job title on a CV.
Most engagements cover recommendation, fraud detection, demand forecasting and credit scoring. Engineers build supervised, ranking and forecasting models with rigorous validation, then serve them behind APIs or batch jobs tied to a business KPI agreed at the start, such as fraud loss avoided or forecast error reduced.
They monitor input distributions, prediction quality, bias and performance decay continuously, using MLflow, SageMaker or Vertex monitoring. When metrics cross agreed thresholds, automated retraining triggers fire, and new versions go through A/B or shadow testing before they replace the model in service.
All of them, matched to the problem. Tabular fraud and credit models often perform best with XGBoost or scikit-learn; sequence and ranking problems may justify PyTorch or TensorFlow; very large datasets run on Spark ML. Engineers justify each choice with validation results rather than preference.
For 1 or 2 models, yes. Our ML Engineers build reproducible pipelines with lineage, register models and set up monitoring themselves. Once you run many models or need shared GPU platforms and feature stores, adding an MLOps / ML Platform Engineer keeps delivery fast and governance consistent.
Add this role to a squad, see your quote instantly, and hold profiles free for 72 hours.
Build your squad online
No card needed · 72 h free hold · No-bill trial: 5 working days self-serve, 10 in the Enterprise lane.
20 minutes with a solutions architect
No sales script.