AI​/ML Engineer – Systems Intelligence & Automation

AI/ML Engineer – Systems Intelligence & Automation Client - Remote Long Term

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).

Strong background in at least one of:

• Graph traversal, relationship modeling, or knowledge/graph-based reasoning, or
• Dependency analysis, causal inference, or multi-step flow/root cause analysis.
• Experience developing and integrating APIs for model inference, including real-time or near-real-time analytics use cases.
• Demonstrated experience with at least one of: anomaly detection, pattern recognition, classification, or clustering on operational/observability/governance-style datasets.
• Practical experience with LLMs or conversational AI for guided workflows, prompt/response design, and structured output generation.
• Strong data engineering and feature engineering skills to prepare, validate, and enrich large datasets for AI workloads.

Nice-to-Have Skills

• Knowledge graphs or graph databases (e.g., Neo4j) used for service, asset, or policy reasoning.
• Explainable AI (XAI) techniques and building auditable, traceable decision paths.
• Familiarity with cloud governance, compliance, or risk datasets and concepts.
• Experience integrating with ITSM, workflow, or incident management platforms (e.g., Service Now, Jira).
• Prior work on reducing MTTR