AI/ML Platform Engineer

Realtime Recruitment; Plano (TX); On-site; USD 100,000 - 130,000; Full time

Job summary

A technology recruitment firm in Plano, TX is seeking an AIOps Platform Engineer with expertise in machine learning and cloud engineering. The ideal candidate will design and scale observability solutions using advanced ML technologies. Responsibilities include developing algorithms, integrating observability tools, and applying MLOps practices. Candidates should have 5–8+ years of experience in ML and cloud environments. This role is full-time and mid-senior level.

Qualifications

• 5–8+ years of experience in ML, cloud infrastructure, and observability.
• Strong experience with machine learning for observability.
• Familiarity with Git, Agile practices, and modern testing frameworks.

Responsibilities

• Design, develop, and deploy ML algorithms and GenAI integrations for observability data.
• Implement LLM-based solutions, including prompt engineering and fine-tuning.
• Collaborate cross-functionally to operationalize AI/ML capabilities into observability workflows.

Skills

Machine learning for observability

Time series forecasting

Anomaly detection

Event classification

Python

TensorFlow

Google Cloud Platform

Microsoft Azure

Git

Agile practices

Tools

TensorFlow

PyTorch

scikit-learn

Prometheus

Grafana

Elasticsearch

SQL/NoSQL DBs

Job description

Direct message the job poster from Realtime Recruitment

Type: Full-Time

Experience Level: Mid to Senior (5–8+ years)

About the Role

We are looking for a skilled AIOps Platform Engineer to design, implement, and scale intelligent observability solutions using cutting-edge ML and GenAI technologies. This role combines deep machine learning expertise—particularly in time series forecasting, anomaly detection, and event classification—with strong cloud engineering and observability experience.

You will help build a seamless, data-driven operational ecosystem that supports automation and actionable insights across large-scale infrastructure.

Responsibilities

• Design, develop, and deploy ML algorithms and GenAI integrations for observability data.
• Implement LLM-based solutions, including prompt engineering, fine-tuning, and RAG (retrieval-augmented generation).
• Integrate observability tools within modern cloud environments (Google Cloud & Azure).
• Build and manage data pipelines and storage solutions (Prometheus, Grafana, Elasticsearch, SQL/NoSQL DBs).
• Deploy and scale ML solutions in the cloud, ensuring high availability and performance.
• Collaborate cross-functionally to operationalize AI/ML capabilities into observability workflows.
• Apply MLOps practices, CI/CD, testing frameworks, and Agile methodologies.

Required Skills

• Strong experience with machine learning for observability:
  • Time series forecasting
  • Anomaly detection
  • Event classification and correlation
• Proficiency in Python (R is a plus) and frameworks like TensorFlow, PyTorch, and scikit-learn
• Expertise with cloud services on Google Cloud Platform (GCP) and Microsoft Azure
• Hands-on knowledge of observability platforms and tools
• Solid understanding of relational and non-relational databases
• Familiarity with Git, Agile practices, and modern testing frameworks
• Experience with MLOps, GenAI tooling, and operational AI systems

Preferred Qualifications

• 5–8+ years of experience in ML, cloud infrastructure, and observability
• Demonstrated ability to lead or contribute to cross-functional AI/ML integration projects
• Exposure to high-scale, real-time systems in enterprise environments

Seniority level

• Mid-Senior level

Employment type

• Full-time

Job function

• Information Technology

Industries

• Staffing and Recruiting

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