Dynatrace provides software intelligence to simplify cloud complexity and accelerate digital transformation. With automatic and intelligent observability at scale, our all-in-one platform delivers precise answers about the performance and security of applications, the underlying infrastructure, and the experience of all users to enable organizations to innovate faster, collaborate more efficiently, and deliver more value with dramatically less effort. That’s why many of the world’s largest organizations trust Dynatrace to modernize and automate cloud operations, release better software faster, and deliver unrivalled digital experiences.
Dynatrace makes it easy and simple to monitor and run the most complex, hyper-scale multicloud systems. Dynatrace is a full stack and completely automated monitoring solution that can track every user, every transaction, across every application.
The Opportunity:
We’re looking for a Senior Machine Learning Engineer (MLOps) to build and scale production ML services for our Business Insights products. You will be responsible for driving delivery of major projects across both LLM and traditional ML domains, including data pipeline design, model training, deployment, and monitoring, collaborating with Data Science and Software Engineering to uphold standards for reliability, latency, and cost.
**Your Tasks:
Engineering and Architecture**
- Design and implement robust data and ML pipelines for training, deployment, and inference at scale, ensuring reliability, performance, and cost efficiency across cloud environments.
- Deliver production ML services using cloud‑native patterns (e.g., managed services, serverless, container orchestration) optimized for low latency and high throughput.
- Establish MLOps practices: dataset and model versioning, experiment tracking, promotion gates from development to production, and safe rollback or canary strategies.
- Build ETL/ELT workflows with clear schema management, data validation, reproducibility, and performance tuning for large‑scale datasets.
- Implement strategies for scalable inference, including caching, batching, autoscaling, and hardware‑aware optimizations to meet service‑level objectives.
- Set technical direction for ML service architecture and pipeline design, ensuring scalability and portability across platforms.
Operations, Reliability, and Governance
- Instrument services with metrics, logs, and traces; maintain dashboards and alerts for latency, throughput, errors, drift, and cost.
- Run offline and online evaluations for accuracy, drift, stability, and cost; maintain golden datasets and automated promotion gates.
- Own lifecycle management: training/retraining schedules, deployment procedures, incident playbooks, and post‑incident reviews.
- Implement robust access controls, secrets management, data governance, and auditability across platforms.
Minimum Requiremnets:
- Professional Python: 5+ years writing production‑quality code with testing/packaging and ML/DS libraries (MLflow, FastAPI, scikit‑learn, PyTorch or TensorFlow).
- MLOps: 3+ years with model registries, experiment tracking, promotion gates, and safe deployment strategies.
- Data engineering: 3+ years building reliable ETL/ELT, schema evolution, data validation, and performance tuning on large‑scale datasets.
- CI/CD and IaC: 3+ years designing and owning build/test/deploy pipelines, plus infrastructure automation.
- Containers and orchestration: 3+ years operating ML services on Kubernetes or equivalent.
- Communication: clear design docs, ability to explain trade‑offs to technical and non‑technical stakeholders.
- Education: Master’s degree or equivalent practical experience in CS/Engineering/Math or related field.
Preffered Requirements:
- Experience with SQL‑centric data platforms (e.g., Snowflake) or cloud ML workloads (AWS/GCP/Azure).
- Observability and monitoring integration (Dynatrace or similar).
- Workflow orchestration (Prefect, Airflow) and CI tools (Jenkins, GitHub Actions).
- Streaming and near real‑time patterns (Kafka, Kinesis).
- Security and privacy: PII handling, audit trails, policy enforcement.
- Domain: telemetry and observability, time‑series modelling, anomaly detection.