Machine Learning Engineer

The Role We are seeking a

Senior MLOps / ML Platform Engineer

to bridge the gap between AI research and production system architecture. You will own our internal ML infrastructure, optimize real-time data pipelines, and eliminate platform friction for our Data Science teams. If you treat model deployment, observability, and container orchestration as a rigorous engineering discipline, let's talk.

Core Responsibilitie sPlatform Architecture

: Scale our enterprise MLOps ecosystem using Databricks, MLflow, Seldon Core, or Triton to manage the full model lifecycle .Backend Engineering

: Build robust, low-latency microservices capable of handling massive streams of multivariate time-series sensor telemetry .Inference Optimization

: Build custom pre/post-processing pipelines and optimize execution paths for low latency .Observability

: Implement production monitoring frameworks (Prometheus, Grafana) for drift detection and anomaly checks .Developer Experience

: Create internal tools and SDKs to let data scientists register and version models seamlessly

. What We Are Looking F orExperienc

e: 5+ years shipping production-grade backend systems and operationalizing applied ML models at scal e.The Stac

k: Advance

d Pyth

on coupled with production mastery of a lower-level language

(Go, Rust, C++, or Ja

va ).Infrastructur

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

s: Familiarity with message brokers (Kafka, Redpanda, MQTT) and time-series or distributed database s.First-Principles Mindse

t: Focus on clean/SOLID code and independent, deterministic engineering over AI shortcut

s. Nice-to-Ha vesMaster's/PhD in Intelligent Systems, Spatio-Temporal Data, Signal Processing, or Distributed Computi ng.Experience handling biomedical, biochemical, or industrial IoT sensor datase ts.Security expertise (API security, vulnerability mapping, or DevSecOps complianc

e).