AI/ML Engineer Systems Intelligence & Automation

Overview

Remote
Depends on Experience
Contract - W2
Contract - Independent
Contract - 12 Month(s)

Skills

Artificial Intelligence
Data Modeling
IT Service Management
Machine Learning (ML)
Python

Job Details

Title: AI/ML Engineer Systems Intelligence & Automation

Visa Status: GC

Location: Remote 100%

Role Overview
Hiring for a single AI/ML Engineer role responsible for building intelligent systems that reason over complex, structured data to drive deterministic decisions, root-cause diagnosis, asset and service intelligence, and workflow automation across large-scale environments. The engineer will design end-to-end AI solutions from data modeling and ML/LLM development to APIs and integrations that improve accuracy, observability, and automation across policy, cloud, service, and request workflows.Key Responsibilities
Design and implement AI/ML and rule-based hybrid systems that evaluate structured inputs (e.g., IPs, ports, services, assets, policies) and return deterministic, explainable outcomes.
Build reasoning and flow-analysis engines that trace multi-step dependencies, identify failure points, and generate concise, human-readable diagnostic summaries.
Develop AI models and validation engines to detect conflicts, anomalies, overlaps, and misconfigurations in large-scale allocation, asset, and configuration datasets.
Model relationships between endpoints, services, dependencies, and cloud assets using graph- or relationship-based approaches, supporting service mapping and health intelligence.
Ingest and correlate telemetry, observability, and governance data to assess health, detect degradation patterns, and support audit, compliance, and MTTR reduction goals.
Build APIs and chat/assistant-style interfaces that expose these intelligence capabilities for real-time and on-demand workflows and ITSM/enterprise platforms.
Implement explainability and audit layers that surface rule paths, reasoning traces, and decision justifications for technical and non-technical stakeholders.
Continuously refine models and workflows using user feedback, monitoring signals, and performance metrics.

Must-Have Skills & Experience
3 5+ years of hands-on AI/ML engineering experience with strong Python skills.
Proven experience building production-grade AI systems that combine rule-based logic with ML (e.g., validation engines, decision engines, or policy-as-code style systems).
Experience working with structured and semi-structured datasets (policies, telemetry, asset inventories, allocations, service maps, or similar).

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