Hire ML Engineers
Add senior ML engineers to your team in 2 to 4 weeks, without slowing delivery down to run a long hiring cycle. Softwarium provides dedicated ML engineers and full ML teams for hire, available via staff augmentation, dedicated team, and fixed-scope engagement models.
Microsoft Gold Partner since 2010 | Clutch: 5.0 
|
25 years | 120+ engineers
Signs Your Team Needs
an ML Engineer Now
A key engineer left, and you need continuity fast.
- Your ML roadmap keeps slipping because no one owns execution.
- Your models are live, but nobody monitors drift or retraining.
- You have a data scientist, but no engineer to productionize models.
- A new ML initiative starts now, and in-house hiring is too slow.
- Your team cannot cover NLP, vision, and MLOps at once.
ML Engineers and Specialists
Available to Hire
ML Engineer
Mid / Senior / Lead
Core skills: Python, TensorFlow or PyTorch, model serving APIs
Hire this role when you already have data and a defined use case, and you need someone to build, train, optimize, and deploy models into real product workflows.
Data Scientist
Mid / Senior
Core skills: statistical modeling, scikit-learn, experiment design
Hire this role when the business question is clear, but the right ML approach is not. This is the profile that turns messy data into a workable path forward.
MLOps Engineer
Mid / Senior
Core skills: MLflow, Kubeflow, Docker and Kubernetes
Hire this role when your models are in production, or close to it, and you need repeatable pipelines, monitoring, retraining, and reliable release processes around ML.
NLP Engineer
Mid / Senior
Core skills: Hugging Face, spaCy, semantic search
Hire this role when your product works with documents, support conversations, contracts, OCR output, knowledge search, or other text-heavy workflows that need language-aware systems.
Computer Vision Engineer
Mid / Senior
Core skills: OpenCV, YOLO, Google Vision API
Hire this role when your use case depends on images, scanned documents, object detection, visual classification, OCR, or video analysis that must work reliably in production.
Dedicated ML Team
Senior-led team
Core skills: ML engineering, data science, MLOps
Choose this model when you are building an ML product or platform from scratch, need multiple roles working together, and want a team assembled around scope, timeline, and delivery stage.
Flexible Engagement Models
for Every Stage
ML Staff Augmentation
One or more ML engineers join your team under your direction. You manage priorities, architecture, and day-to-day work, while Softwarium handles payroll, operations, and retention.
Dedicated ML Team
You get a pre-assembled team built around your project, often combining an ML engineer, a data scientist, and an MLOps engineer. This model fits greenfield ML initiatives and larger delivery streams that need a technical lead and a clear operating rhythm.
Fixed-Scope ML PoC
Start with a defined 4 to 8 week proof of concept when you want to validate data readiness, technical feasibility, or a narrow use case before expanding into a longer engagement.
Which model
fits your roadmap?
How to Hire an ML Engineer from Softwarium
1Discovery Call
We start with a 30-minute call to understand your stack, ML goals, delivery stage, and team structure. That gives us the context to match the right profile, not just the nearest available resume.
2Engineer Matching
Within 5 business days, Softwarium sends 2 to 3 pre-vetted senior ML engineer profiles that match your technical needs, domain context, and engagement model.
3You Interview
You speak with candidates directly. Technical interview, team-fit discussion, no intermediaries sitting in the middle of the conversation.
4Onboarded and Productive
Once you approve the candidate, your engineer joins your workflow, tools, and sprint cadence. Hiring an ML engineer from Softwarium typically takes 2 to 4 weeks from initial consultation to onboarded engineer, with no long-term commitment required beyond the engagement model agreed.
What Your First 30 Days Look Like
Week 1Environment setup, codebase access, architecture context, data source review, and first sprint planning. Your engineer joins Slack, Jira, GitHub, and your regular delivery ceremonies.
Weeks 2 and 3First deliverables move into motion: model work, pipeline tasks, evaluation, integration tickets, or production hardening. Daily standups and asynchronous updates establish pace and visibility.
Week 4You review the first sprint output, confirm working cadence, and adjust backlog priorities. By this point, communication rhythm, ownership boundaries, and delivery velocity are established.
Why Engineering Teams Hire
ML Talent from Softwarium
Tech Stack Our ML Engineers Work In
Softwarium's ML engineers are senior specialists with expertise in Python, TensorFlow, PyTorch, Azure ML, Amazon SageMaker, and Google Vertex AI.
Languages
Python, R, Julia
ML Frameworks
TensorFlow, PyTorch, scikit-learn, Keras, XGBoost
Cloud ML Platforms
Azure Machine Learning, Amazon SageMaker, Google Vertex AI
NLP Tools
BERT, Hugging Face, spaCy
MLOps
MLflow, Kubeflow, Docker, Kubernetes, Airflow
Data Engineering
Spark, Kafka, Airflow, FastAPI
ML Work Our Engineers Have Shipped
Clinical Decision Support System for Psychiatry
Softwarium’s ML engineers built a production clinical decision support system for a healthcare provider, combining explainable AI with EHR integration. The goal was not a research prototype, but a system clinicians could use within real workflows to reduce medication errors and improve diagnostic accuracy.
Applied AI and ML R&D for ProTitleUSA
For a national title search company, Softwarium benchmarked and selected Google Vision and Vertex AI for document-heavy workflows. The engagement focused on applied AI and ML R&D that could move toward production, and it achieved up to 70% faster document review in the target process.
ML-Powered Deduplication for Salesforce AppExchange
Softwarium developed custom ML algorithms for a Salesforce AppExchange product focused on deduplication. The result was positioned as the only ML-powered dedupe tool on the platform, which gave the product a sharp functional differentiator in a crowded category.
Questions About Hiring ML Engineers
- How much does it cost to hire an ML engineer?
Full-time ML engineers in the US often cost $130,000 to $180,000 per year in salary alone, before benefits, recruiting, and management overhead. ML staff augmentation from Eastern Europe typically costs 30% to 50% less, while still giving you direct access to experienced engineers. Softwarium provides a transparent cost breakdown during the discovery call, so you can budget with clear expectations and no hidden fees.
- How quickly can I hire an ML engineer from Softwarium?
In most cases, from first contact to onboarded engineer takes 2 to 4 weeks. The path is straightforward: discovery call, profile matching within 5 business days, candidate interview, then onboarding. That timeline is possible because Softwarium works from a pre-vetted pool of senior ML engineers rather than starting a search from zero.
- What is the difference between staff augmentation and a dedicated ML team?
With staff augmentation, one or more ML engineers join your existing team and work under your management. With a dedicated ML team, Softwarium assembles a small squad, often an ML engineer, data scientist, and MLOps engineer, and coordinates delivery through a project lead. Staff augmentation fits established teams that need capacity or a missing skill. A dedicated team fits greenfield ML work or larger initiatives that need a structured delivery unit.
- Can I interview ML engineers before hiring them?
Yes. After Softwarium sends you 2 to 3 matched profiles, you interview candidates directly and make the approval decision yourself. You only move forward with engineers you want on your team.
- What seniority levels of ML engineers can I hire?
Softwarium provides mid-level, senior, and lead ML engineers. The majority of the ML engineering team is senior or above, with substantial production experience. For dedicated teams, a lead or principal engineer can anchor architecture and technical direction.
- Do Softwarium ML engineers work in my time zone?
Softwarium’s engineers are Europe and US-based. Our Europe-based team works typically 9 a.m. to 6 p.m. EET. For US clients, that usually creates a 4 to 7 hour overlap with EST or PST, which is enough for standups, planning, reviews, and critical meetings. The day-to-day working style is asynchronous-first, using tools like Slack, Jira, and GitHub.
- What happens if an ML engineer leaves mid-project?
Softwarium manages engineer retention as part of the engagement, because the engineer is employed by Softwarium, not contracted ad hoc through a marketplace. If a transition is ever needed, Softwarium provides a screened replacement and manages the handover with documentation and continuity in mind. The 10+ year Synovos relationship is a strong signal that long-term delivery stability is built into the operating model.
- What ML platforms and tools do Softwarium engineers work with?
Softwarium’s ML engineers work across Azure Machine Learning, Amazon SageMaker, and Google Vertex AI, alongside the core tooling most product teams already use. The stack includes Python, TensorFlow, PyTorch, scikit-learn, Hugging Face, BERT, spaCy, MLflow, Kubeflow, Docker, and Kubernetes. Stack matching is confirmed during the discovery call, so you know the engineer fits your environment before onboarding starts.
Ready to Hire
Your ML Engineer?
If you need ML execution capacity, production ML experience, or a dedicated team around a new initiative, Softwarium gives you a practical path to start quickly and stay in control of delivery.
