AI/ML Engineer

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).