Introduction
Most AI projects fail not because of bad models, but because companies don't staff the full lifecycle. Here are the 6 roles that take AI from proof-of-concept to production.
1. Data Engineer
You can't train models on broken data. Data engineers build the pipelines and platforms that feed ML systems. They own data quality, lineage, governance, and scale. Without a strong data engineer, your ML team spends 60% of its time fixing data issues instead of building models. They work with Spark, Kafka, Airflow, Snowflake, dbt, and Databricks.
2. ML Engineer
The bridge between data science experiments and production systems. ML engineers take models from notebooks and deploy them as APIs, batch jobs, or embeddings. They own model packaging, reproducibility, versioning, and monitoring. They work with PyTorch, TensorFlow, model serving frameworks, and containerization.
3. MLOps Engineer
CI/CD for models. MLOps engineers build the infrastructure for model training, testing, versioning, and deployment automation. They own monitoring, alerting, and drift detection. They work with Kubeflow, SageMaker, Vertex AI, Databricks, Kubernetes, and monitoring platforms. This role has become critical as organizations scale from one model to dozens.
4. Embedded AI Engineer
When your AI needs to run on-device, not in the cloud. Embedded AI engineers deploy models to edge devices, microcontrollers, and real-time systems. They work with TinyML, ONNX, TensorRT, real-time constraints, and hardware integration. This role is critical for robotics, autonomous systems, manufacturing, and defence applications.
5. AI Security Engineer
Protecting models and data from adversarial attacks and compliance risks. AI security engineers conduct threat modeling, adversarial testing, and governance automation. They work with OWASP for ML, NIST AI RMF, and MITRE ATLAS frameworks. This role is becoming table stakes as regulatory pressure mounts.
6. AI/ML Leader
Someone who's done this before and can hire, mentor, and set technical direction. This person owns the team structure, technology choices, and execution on the AI strategy. They've shipped production AI systems and understand the full stack. This is often a Head of AI, VP Engineering, or Technical Director role.
Conclusion
Sonitec specializes in recruiting across all 6 pillars of the AI lifecycle. We help organizations move from scattered AI initiatives to coordinated, production-grade teams that deliver real business impact.
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