Machine Learning Engineer

Senior MLOps / Machine Learning Platform Engineer

Location:

Hybrid / Remote Options

Industry:  Deep Tech / Biotech / Industrial Analytics

Employment Type:

Full-time

The Role
We are seeking a  Senior MLOps / ML Platform Engineer  to own our internal ML infrastructure, optimize real-time data pipelines, and scale our system architecture. If you treat model deployment, observability, and container orchestration as a rigorous engineering discipline rather than relying on AI shortcuts, let’s talk.

Core Responsibilities

Platform Architecture:  Scale our enterprise MLOps ecosystem (Databricks, MLflow, Seldon, or Triton) across the full model lifecycle.

Backend Engineering:  Build low-latency microservices capable of handling massive streams of multivariate time-series sensor telemetry.

Inference Optimization:  Design custom pre/post-processing pipelines and optimize execution paths for low latency.

Observability:  Deploy production monitoring frameworks (Prometheus, Grafana) for drift detection and anomaly checks.

Developer

Experience:

Create internal tools and SDKs to let data scientists deploy and version models seamlessly.

What We Are Looking For

Experience:

5+ years shipping production-grade backend systems and operationalizing applied ML models at scale.

The Stack:  Advanced  Python  coupled with production mastery of a lower-level language ( Go, Rust, C++, or Java ).

Infrastructure:  Deep experience with Kubernetes, Helm, Docker, and cloud data platforms (Databricks, Spark).

Data Streams:  Familiarity with message brokers (Kafka, Redpanda, MQTT) and time-series or distributed databases.

First-Principles Mindset:  Focus on clean/SOLID code and independent, deterministic engineering over AI shortcuts.

Nice-to-Haves

Master's or PhD in Intelligent Systems, Spatio-Temporal Data, Signal Processing, or Distributed Computing.

Experience handling biomedical, biochemical, or industrial IoT sensor datasets.

Security expertise (API security, vulnerability mapping, or Dev Sec Ops  compliance)

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