AI ML Engineer

Introduction

This role focuses on the design, implementation, and operation of high-throughput, real-time AI and computer vision systems in a manufacturing environment. The AI ML Engineer will work on distributed Python services, real-time data/vision streaming, performance engineering, observability, and automated remediation. This position collaborates closely with cross-functional engineering teams to help deliver reliable, secure, and scalable production systems.

Responsibilities

Backend & Platform Engineering

• Design and implement backend services using Python for high-concurrency, low-latency workloads.
• Configure and maintain containerized services deployed on Kubernetes using Helm.
• Tune resource limits/requests, autoscaling, and networking for stateful and streaming workloads.
• Implement secure deployment patterns using enterprise registries and CI/CD pipelines.

Real-Time Data & Vision Streaming

• Build and maintain ingestion services for real-time data streams, including industrial video protocols.
• Integrate multi-camera and multi-stream inputs into Python backends for monitoring, diagnostics, and analytics.
• Optimize streaming, buffering, and processing behavior to meet strict latency targets.

Numerical & Statistical Computing

• Implement and optimize matrix-heavy and numerical workloads using NumPy, SciPy, and OpenCV.
• Translate statistical methods into robust, production-grade code.
• Design calculations to distinguish normal process variation from true degradation or drift in data and signals.

Automated Remediation & Diagnostics

• Implement backend decision logic to separate software-correctable issues from physical or environmental issues.
• Develop automation scripts and workflows for programmatic fixes.
• Generate precise diagnostic payloads for maintenance and operations teams.

Performance, Reliability & Resilience

• Design and run load, stress, and soak tests that include high-FPS streams, bursty workloads, and multi-session interactions.
• Implement and tune rate limiting, request throttling, queuing, and back-pressure strategies.
• Apply resiliency patterns for failed or partial executions.

Observability & Monitoring

• Configure and extend observability stacks for metrics, logs, and distributed tracing.
• Enable visualization and inspection of complex execution graphs and data flows.
• Contribute to definition and tracking of SLOs/SLIs around system health.

APIs & Integration

• Expose clean, stable APIs and schemas for downstream consumers.
• Provide diagnostic outputs to support operator-facing tools.
• Collaborate with frontend and integration teams to ensure APIs are well-documented, versioned, and testable.

Requirements

Required Skills & Experience

• 6+ years in Infrastructure/SRE, Core Platform Engineering, MLOps, Vision/Edge Platform Engineering, or high-throughput data/backend systems.
• Experience delivering and supporting production systems with strict performance, reliability, and availability requirements.

Python & Backend

• Expert-level production Python skills.
• Strong understanding of memory management, concurrency, and profiling in Python.

Containers, Kubernetes & CI/CD

• Deep experience with Docker and Kubernetes.
• Practical experience with Helm for templating and deploying applications.
• Hands-on experience with CI/CD tools for automated builds, tests, deployments, and rollbacks.

Streaming & Vision

• Hands-on experience ingesting and processing industrial camera/video streams.
• Strong proficiency with OpenCV and image/matrix operations.

Numerical & Statistical Skills

• Advanced proficiency with NumPy and SciPy.
• Experience implementing statistical techniques in production pipelines.

Performance, Testing & Observability

• Proven use of load and stress testing tools for data-heavy or streaming APIs.
• Experience with observability tools and concepts.

Collaboration & Ways of Working

• Experience working as part of a cross-functional engineering team.
• Strong communication skills for documenting designs, APIs, diagnostics, and test results.
• Ability to work in an iterative environment with clear deliverables, reviews, and handoffs.

Preferred Qualifications

• Experience with industrial IoT, manufacturing environments, or other edge/plant-floor systems.
• Background in automated rollback, closed-loop control, or other remediation/alerting frameworks.
• Familiarity with modern AI/ML or LLM/agent systems from an infrastructure or performance perspective.