Senior ML Ops Engineer

We are looking for a Senior MLOps Engineer to own and build the end-to-end machine learning lifecycle with a special focus on secure reliable deployment to edge devices.

You are a systems thinker and a hands-on engineer. You will be responsible for everything from the initial data pipeline to the final on-device model verification. You will design our data-labeling feedback loops build the CI / CD pipelines that convert and deploy models and implement the monitoring systems that tell us how those models areactuallyperforming in the wildboth in terms of speed and quality.

This role is a unique blend of data engineering DevOps ML security and performance optimization. You will be the engineer who ensures our models are not only fast but also trusted secure and continuously improving.

Key Responsibilities

1. Data & Labeling Lifecycle Management :

Architectandimplementscalable data processing pipelines for ingesting validating and versioning massive datasets (e.g. using DVC Pachyderm or custom S3 / Artifactory solutions).

Design and buildthe infrastructure for ourHuman-in-the-Loop (HITL)andAI-in-the-Loop (Active Learning)data labeling systems. This includes creating the feedback loops that identify high-value data for re-labeling.

Conduct deepdata analysisto identify data drift dataset bias and feature drift ensuring the statistical integrity of our training and validation sets.

2. On-Device Model Monitoring :

Design and deploylightweight on-device telemetry agents to monitorinference qualityandconcept drift not just operational metrics.

Implement statistical monitoring on model outputs (e.g. confidence distributions output ranges) and create automated alerting systems to flag model degradation.

Build the backend dashboards (e.g. Grafana custom dashboards) to aggregate and visualize on-device performance and quality metrics from a fleet of edge devices.

3. Model Conversion & Deployment (CI / CD for ML) :

Build and maintaina robust CI / CD pipeline (e.g. GitLab CI Jenkins GitHub Actions) that automates model training conversion quantization (PTQ / QAT) and packaging.

Manage themodel conversionprocess translating models from PyTorch / TensorFlow into optimized formats (e.g. ONNX TFLite) for ourAI inference engine.

Orchestrate model deployment to edge devices managing model versioning and enabling reliable Over-the-Air (OTA) updates.

4. On-Device Model Security & Verification :

Implement a robustmodel verificationframework using cryptographic signatures to ensureentity verification(i.e. that the model running on-device is the one we deployed).

Design and apply security protocols (e.g. secure boot model encryption) toprevent model injection attacksand unauthorized model tampering on the device.

Collaborate with firmware and hardware security teams to ensure our MLOps pipeline adheres to a hardware root of trust.

5. Performance Optimization :

Analyze and optimize ML model performance for our specificAI inference engine.

Applygraph-level optimizations(e.g. operator fusion pruning) andOP-level optimizations(e.g. rewriting custom ops leveraging hardware-specific data types) to maximize throughput and minimize latency.

Qualifications :

5 yearsof experience in MLOps DevOps or Software Engineering with a focus on ML systems.

Proven experience building and managing thefull MLOps lifecycle from data ingestion to production monitoring.

Strong programming skills in Python and deep experience with ML frameworks (e.g. PyTorch TensorFlow).

Demonstrable experience withmodel conversion and optimizationfor edge devices (e.g. using ONNX TFLite TensorRT orApache TVM).

Strong understanding ofdata engineeringprinciples and experience with data-labeling strategies (HITL / Active Learning).

Excellent understanding of CI / CD principles and tools (e.g. Git Docker GitLab CI).

Preferred Qualifications (The Plus Factors)

Hands-on experience withKubernetes (K8s)for MLOps orchestration (e.g. Kubeflow Argo Workflows).

Familiarity with GPU scheduling and virtualization platforms such asRun : AI.

Proficiency in managing MLOps infrastructure on at least one majorcloud platform(AWS GCP Azure).

Experience with embedded systems security cryptographic signing or hardware security modules (HSMs).

Experience in C for deploying high-performance inference code.

Additional Information :

Axiado is committed to attracting developing and retaining the highest caliber talent in a diverse and multifaceted environment. We are headquartered in the heart of Silicon Valley with access to the worlds leading research technology and talent.

We are building an exceptional team to secure every node on the internet. For us solving real-world problems takes precedence over purely theoretical problems. As a result we prefer individuals with persistence intelligence and high curiosity over pedigree alone. Working hard and smart continuous learning and mutual support are all part of who we are.

Axiado is an Equal Opportunity Employer. Axiado does not discriminate on the basis of race religion color sex gender identity sexual orientation age non-disqualifying physical or mental disability national origin veteran status or any other basis covered by appropriate law. All employment is decided on the basis of qualifications merit and business need.

Remote Work : No

Employment Type : Full-time

Key Skills

APIs,C / C++,Computer Graphics,Go,React,Redux,Node.js,AWS,Library Services,Assembly,GraphQL,High Voltage

Experience : years

Vacancy : 1