Need people with a product mindset.
Strong Technical acument:
must have strong Pyhton coding, and ML
Curosr->IDE->CODEX
HEAVY HANDS ON ROLE:
find a problem->come up w/ recommendations, not just problems
CURIOSITY DRIVEN
ML MODELING EXPERIENCE
This role focuses on the design, implementation, and operation of high?throughput, real?time AI and computer vision systems in a manufacturing environment. Work centers on distributed Python services, real?time data/vision streaming, performance engineering, observability, and automated remediation. The position collaborates closely with cross?functional engineering teams to help deliver reliable, secure, and scalable production systems.
Key Responsibilities
Backend & Platform Engineering
- Design and implement backend services using Python (FastAPI/Uvicorn/asyncio) for high?concurrency, low?latency workloads.
- Configure and maintain containerized services (Docker) 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 (e.g., GitHub Actions).
Real?Time Data & Vision Streaming
- Build and maintain ingestion services for real?time data streams, including industrial video protocols (e.g., RTSP).
- Integrate multi?camera and multi?stream inputs into Python backends for monitoring, diagnostics, and analytics.
- Optimize streaming, buffering, and processing behavior to meet strict per?frame or per?request latency targets.
Numerical & Statistical Computing
- Implement and optimize matrix?heavy and numerical workloads using NumPy, SciPy, and OpenCV.
- Translate statistical methods (e.g., distance metrics, anomaly scores, temporal smoothing, thresholds) into robust, production?grade code.
- Design and execute calculations that distinguish normal process variation from true degradation or drift in data and signals.
Automated Remediation & Diagnostics
- Implement backend decision logic that separates software?correctable issues (e.g., configuration, firmware, calibration) from physical or environmental issues (e.g., optics, vibration, misalignment).
- Develop automation scripts and workflows that execute programmatic fixes (e.g., rolling back versions, refreshing baselines, updating configurations).
- Generate precise, localized diagnostic payloads for maintenance and operations teams when physical remediation is required.
Performance, Reliability & Resilience
- Design and run realistic load, stress, and soak tests (e.g., with k6 or Locust) that include high?FPS streams, bursty workloads, and multi?session interactions.
- Implement and tune rate limiting, request throttling, queuing, and back?pressure strategies to protect downstream services.
- Apply resiliency patterns such as retries, circuit breakers, fallbacks, and state?recovery for failed or partial executions.
Observability & Monitoring
- Configure and extend observability stacks (e.g., OpenTelemetry or similar) for metrics, logs, and distributed tracing across services.
- Enable visualization and inspection of complex execution graphs and data flows to support diagnostics and performance tuning.
- Contribute to definition and tracking of SLOs/SLIs around latency, throughput, error rates, and system health.
APIs & Integration
- Expose clean, stable APIs and schemas (e.g., JSON payloads) for downstream consumers, including UI applications and other backend services.
- Provide deterministic structures for diagnostic outputs (e.g., spatial heatmaps, ROI metrics, temporal trends) to support operator?facing tools.
- Collaborate with frontend and integration teams to ensure APIs are well?documented, versioned, and testable.
Required Skills & Experience
Core Experience
- 6+ years in one or more of the following: 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, including:
- Asynchronous programming with asyncio.
- Building APIs with FastAPI (or similar ASGI frameworks).
- Tuning Uvicorn/gunicorn or equivalent servers for high?concurrency workloads.
- Strong understanding of memory management, concurrency, and profiling in Python.
Containers, Kubernetes & CI/CD
- Deep experience with Docker (image design, security, performance, multi?stage builds).
- Strong Kubernetes knowledge: workloads, services, ingress, stateful workloads, autoscaling, and resource tuning.
- Practical experience with Helm for templating and deploying applications.
- Hands?on experience with CI/CD (e.g., GitHub Actions or similar) for automated builds, tests, deployments, and rollbacks.
Streaming & Vision
- Hands?on experience ingesting and processing industrial camera/video streams (e.g., RTSP or similar protocols).
- Experience working with real?time streaming or event?driven architectures in production environments.
- Strong proficiency with OpenCV (or equivalent) and image/matrix operations.
Numerical & Statistical Skills
- Advanced proficiency with NumPy and SciPy for vectorized, high?performance numerical computing.
- Experience implementing statistical techniques (e.g., distance measures, anomaly detection, trend analysis) in production pipelines.
- Ability to profile and optimize algorithmic hot paths and matrix operations.
Performance, Testing & Observability
- Proven use of load and stress testing tools (k6, Locust, or similar) for data?heavy or streaming APIs.
- Experience with observability tools and concepts, including metrics, logging, and distributed tracing (OpenTelemetry or equivalent).
- Demonstrated ability to analyze performance bottlenecks and implement targeted improvements.
Collaboration & Ways of Working
- Experience working as part of a cross?functional engineering team within an established architecture and technical direction.
- 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 (cameras, sensors, PLCs, etc.).
- 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 (e.g., handling tool?calling, long?running workflows, or token?streaming patterns).
