AI/ML Engineer

Quick Facts

• Role: AI/ML Engineer

Description

Design, implement, and operate high-throughput, real-time AI and computer vision systems in a manufacturing environment. Build distributed Python services for low-latency, high-concurrency workloads, real-time data/vision streaming, and robust numerical/statistical processing. Ensure reliability through performance engineering, observability, and automated diagnostics/remediation.

Responsibilities

• Backend & Platform Engineering: Design and implement Python backend services (FastAPI/Uvicorn/asyncio); containerize with Docker; deploy on Kubernetes using Helm; tune autoscaling/resources/networking; implement secure deployment patterns with CI/CD.

• Real-Time Data & Vision Streaming: Build ingestion services for real-time streams (e.g., RTSP); integrate multi-camera/multi-stream inputs; optimize buffering and processing to meet per-frame/per-request latency targets.

• Numerical & Statistical Computing: Implement and optimize NumPy/SciPy/OpenCV workloads; translate statistical methods into production-grade code; detect normal variation vs. degradation/drift.

• Automated Remediation & Diagnostics: Separate software-correctable vs physical/environmental issues; automate fixes (rollback, baseline refresh, config updates); generate localized diagnostic payloads for maintenance.

• Performance, Reliability & Resilience: Run load/stress/soak tests (k6/Locust); implement throttling/queuing/back-pressure; apply retries, circuit breakers, fallbacks, and state recovery.

• Observability & Monitoring: Configure distributed tracing/metrics/logs (e.g., OpenTelemetry); support diagnostics with execution graph visualization; define/track SLOs/SLIs.

• APIs & Integration: Expose stable, well-documented APIs and schemas (e.g., JSON) for UI and other services; provide deterministic diagnostic outputs; collaborate to ensure versioned, testable interfaces.

Requirements

• 6+ years of experience in Infrastructure/SRE, Core Platform Engineering, MLOps, Vision/Edge Platform Engineering, or high-throughput data/backend systems.

• Production experience meeting strict performance, reliability, and availability requirements.

• Expert production Python (asyncio), FastAPI/ASGI development, and tuning Uvicorn/gunicorn (memory management, concurrency, profiling).

• Deep Docker experience and strong Kubernetes knowledge; practical Helm; hands-on CI/CD (e.g., GitHub Actions) for builds/tests/deployments/rollbacks.

• Hands-on industrial streaming ingestion (e.g., RTSP), production real-time/event-driven architectures; strong OpenCV and image/matrix operations.

• Advanced NumPy/SciPy; experience implementing statistical techniques in production; ability to profile and optimize algorithmic hot paths.

• Load/stress testing with k6/Locust (or similar); observability concepts and ability to analyze performance bottlenecks.

• Cross-functional collaboration, strong communication for design/API/diagnostic documentation, and iterative delivery.

Preferred Qualifications

• Experience with industrial IoT/manufacturing/edge systems (cameras, sensors, PLCs).

• Background in automated rollback, closed-loop control, or remediation/alerting frameworks.

• Familiarity with modern AI/ML or LLM/agent systems from an infrastructure/performance perspective (e.g., tool-calling, long-running workflows, token-streaming).