AI​/ML Observability Engineer

VISA INDEPENDENT CANDIDATES ONLY !

We are seeking a passionate and hands‑on AI/ML Engineer to accelerate our Enterprise Observability strategy. This role will design, build, and operationalize AI/ML capabilities that enhance end to end telemetry pipelines, anomaly detection, intelligent alerting, and proactive system resiliency.

You will work at the intersection of AI/ML engineering, Observability platforms, and automation, developing solutions that improve detection, diagnosis, and prevention of operational issues across distributed systems.

• Design and deploy AI/ML models supporting anomaly detection, baselining, event correlation, and predictive operational analytics.
• Build and integrate AI‑enabled capabilities into enterprise Observability platforms, including Grafana, APM/RUM tools, network telemetry systems, and data observability tools.
• Develop AI Agents that can autonomously triage issues, recommend corrective actions, and initiate automated remediation workflows to reduce recovery time and improve system resilience.
• Implement self‑healing automation using AI‑driven decisioning, integrating with orchestration frameworks, service APIs, and infrastructure automation pipelines.
• Engineer and maintain real‑time and batch data pipelines using Snowflake ML Jobs, Snowflake Cortex, streams, tasks, and UDFs.
• Implement and manage Open Telemetry‑based telemetry ingestion for logs, metrics, traces, and spans across distributed systems.
• Build asynchronous Python APIs and services for model inferencing and operational integration.
• Enhance observability intelligence with AI‑powered capabilities such as root‑cause acceleration, chatbot/search enablement, and automated insights.
• Contribute to SLO/SLI modeling, Golden Signals instrumentation, and Observability NFR adoption.
• Collaborate across engineering, SRE, platform and business teams to embed proactive intelligence and Observability standards throughout the ecosystem.

• Strong proficiency in Python and data science/ML libraries:
  • Num Py
  • Pandas
  • scikit learn
  • Tensor Flow
  • Py Torch
  • Matplotlib
  • Seaborn
• Experience with Generative AI, LLM fine tuning, prompt engineering, RAG pipelines, and LLM evaluation frameworks.
• Expertise in developing and deploying ML models in production (batch & streaming).
• Strong understanding of statistics, time series modeling, and anomaly detection.

• Experience with Open Telemetry for logs, metrics, traces, spans.
• Familiarity with Observability concepts:
  • Golden Signals
  • SLO/SLI design
  • APM
  • RUM
  • Synthetics
  • event correlation
  • baselining
• Experience with Observability tools such as:
  • Grafana (Alloy agents, dashboards, ML capabilities)
  • Dynatrace
  • Monte Carl0 (Data Observability)
  • Netscout
  • Thousand Eyes
  • Solar Winds
  • Net Brain

• Hands on with AWS (Sage Maker, Bedrock), Snowflake ML, Snowflake/Openflow, Snowflake AI Observability tooling.
• Experience building Snowflake data pipelines (streams, tasks, UDFs) – plus for Cortex features.
• Strong understanding of distributed systems and microservices telemetry requirements.

• Experience with automation pipelines, CI/CD, and infrastructure as code patterns supporting Observability adoption.
• Ability to build asynchronous Python APIs or services for model inference and operational integration.

• Experience developing agentic AI systems that analyze telemetry, generate action recommendations, or execute automated operational responses.
• Experience building self‑healing patterns, including automated rollback, service restarts, configuration corrections, and predictive maintenance.
• Experience in Snowflake ML workflows, Snowflake Cortex Agents, and data pipeline automation.
• Exposure to AI‑enabled alerting, RCA automation, and operational self‑healing concepts.
• Experience with large‑scale operational telemetry and multi‑cloud ecosystems.

• Strong analytical thinking and problem solving.
• Excellent communication skills for cross functional collaboration with infrastructure, SRE, engineering, business, and leadership teams.
• Curiosity, continuous learning mindset, and passion for applied AI and Observability.