Builds, trains and deploys ML models into reliable production systems.
Entry
Intermediate
Coding
Very High
Key skills
Career overview
The Machine Learning Engineer builds, trains and deploys machine learning models into reliable production systems: pipelines, serving, monitoring, retraining and scaling. They sit between data science and platform engineering. The role leads to Senior ML Engineer, MLOps Engineer or AI Engineer positions.
Main responsibilities
- Build and train machine learning models
- Train, fine-tune and evaluate models
- Serve model inference at scale
- Build reliable data ingestion pipelines
- Build and maintain CI/CD pipelines
- Monitor platform health, alerts and dashboards
- Keep technical documentation up to date
- Prepare and execute change requests in production
- Analyse application logs to identify root causes
- Automate recurring tasks with scripts
Technical skills
Technologies
Education level
Education requirements vary depending on the company, country and experience. Professional experience, certifications and technical skills can sometimes compensate for formal education.
High School / Bac
Less common
Bac+2 (2-year degree)
Less common
Bac+3 (Bachelor)
Less common
Bac+5 (Master / Engineering)
Common entry point
Experience & coding level
Experience level
Coding level
Useful certifications
Certifications are never mandatory. They are one way among others to prove a skill.
- AWS Machine Learning Specialty
- Google Professional ML Engineer
- Microsoft DP-100 Data Scientist
Where can this career take you?
Multiple paths are possible. Click a role to open its profile.
Direct progression
Possible branches
Where can you go next?
Realistic next career moves, with compatibility and a transition roadmap for each.
Overlap: 48% · Difficulty: Easy
Skills to learn: ML system architecture, Distributed training, Feature stores
View roadmapOverlap: 73% · Difficulty: Medium
Skills to learn: Experiment tracking, IaC (Terraform)
View roadmapOverlap: 37% · Difficulty: Medium
Skills to learn: AI / LLM integration, API design, Prompt engineering
View roadmapOverlap: 25% · Difficulty: Medium
Skills to learn: NLP & transformers, LLM fine-tuning, Embeddings & vector DBs
View roadmapOverlap: 51% · Difficulty: Medium
Skills to learn: OpenCV, CNN / ViT architectures, Data augmentation
View roadmapWhere can you go from here?
A typical progression built from real career relationships. Click any role to open its profile.
Soft skills
- Problem solving
- Rigour and attention to detail
- Analytical thinking
- Autonomy
- Clear communication
- Sense of ownership
Common previous careers
Roles that frequently lead into this career.
Overlap: 26% · Difficulty: Medium
Skills to learn: Model deployment, MLOps, PyTorch / TensorFlow
View roadmapOverlap: 38% · Difficulty: Medium
Skills to learn: Machine learning, MLOps, PyTorch / TensorFlow
View roadmapContinue your career exploration
Keep moving: compare roles, follow a roadmap, read a guide or prepare for an interview.
Ready to explore your next step?
A documented transition from this role to the next.
Machine Learning EngineerSenior ML Engineer
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