About the Team
The AI-first engineering team supports the D&E portfolio, including in-house applications, APIs, event-driven systems, data platforms, and BI tools. It uses AI-powered observability, automated diagnostics, and intelligent incident management to improve system reliability and resolve issues faster.
Responsibilities
•Perform operational diagnostics across batch and streaming data pipelines
•Coordinate issue resolution with platform engineering, Data Engineers, and data domain teams
•Ensure correct use of ITSM tools for incident tracking and lifecycle management
•Monitor critical workflows using Dynatrace
•Orchestrate workflows using StoneBranch
•Use metadata and documentation for troubleshooting and root cause analysis
•Maintain clear ownership, escalation paths, and communication during incidents
•Reduce incident noise and recurring issues through structured problem management
•Own L2/L3 production support across applications, APIs, event-driven systems, and data platforms
•Lead incident triage, diagnosis, resolution, root cause analysis, and post-incident reviews
•Manage incident, problem, and change management processes aligned with ITIL best practices
•Coordinate escalations and resolution of complex production issues with development teams, infrastructure teams, and vendors
•Maintain and improve production verification suites, canary checks, and reliability playbooks
•Own and maintain CI/CD pipelines in Azure DevOps for application and data platform projects
•Provision and manage Azure resources using Infrastructure as Code
•Deploy, scale, and troubleshoot containerized services
•Provision, configure, and maintain development, staging, and production environments
•Implement and maintain deployment strategies that minimize production risk
•Manage cloud resource health, cost optimization, and security posture
•Support microservice architectures, including service discovery, load balancing, scaling, and resilience patterns
•Implement and manage monitoring stacks
•Build and maintain alerting rules, dashboards, and SLO/SLI tracking for services and data pipelines
•Correlate trace IDs across services, diagnose latency bottlenecks, and validate signal fidelity
•Monitor data pipeline health and detect anomalies, latency issues, and data loss
•Aggregate and analyze logs for incident diagnosis
•Support backend services, APIs, event-driven systems, and front-end applications
•Support ETL/ELT pipelines, big data platforms, and data warehouses
•Diagnose database issues, validate transformations, and support migrations
•Manage batch workloads using scheduling and orchestration tools
•Use AI tools to cluster alerts, suggest root causes, and recommend incident resolution steps
•Build and maintain AI-assisted diagnostic scripts for common troubleshooting workflows
•Apply AI/ML for anomaly detection, predictive alerting, and capacity forecasting
•Use AI coding assistants for scripting, automation, configuration, and documentation
•Support AI-based applications and services, including their model serving, inference pipelines, and operational requirements
•Follow responsible AI playbooks for data handling, security, and observability of AI-generated changes
•Write Python scripts for operational automation, data validation, and diagnostic tooling
•Use PowerShell for Windows administration and Azure resource management
•Build self-healing mechanisms and automated remediation where feasible
Requirements
•Production support and incident management experience across in-house applications, APIs, event-driven systems, and data platforms
•Experience with .NET/C# and Python
•Knowledge of structured root cause analysis and post-incident reviews
•Knowledge of ITIL-aligned incident, problem, and change management
•Experience maintaining CI/CD pipelines in Azure DevOps
•Experience with Terraform, Bicep, or ARM templates
•Experience with Docker and Kubernetes (AKS)
•Practical experience with Azure App Services, AKS, Functions, Event Hubs, Storage, Key Vault, Azure SQL, and Azure Monitor
•Knowledge of DNS, load balancers, firewalls, VNets, and private endpoints in Azure
•Experience with Dynatrace, Azure Monitor, or equivalent monitoring tools
•Experience with alerting, dashboards, SLO/SLI tracking, and distributed tracing with OpenTelemetry
•Knowledge of data observability and log aggregation and analysis
•Experience supporting REST/GraphQL APIs, event-driven systems, and WPF/web front-ends
•Experience with ETL/ELT pipelines, PySpark, Spark, Databricks, Palantir Foundry, and data warehouses
•Advanced SQL knowledge; experience with SQL Server and MongoDB
•Experience with StoneBranch, Airflow, or equivalent orchestration tools
•Experience with AI-assisted incident triage, automated runbooks, AIOps, or AI/ML for operational workflows
•Experience supporting AI-based applications and understanding of model serving and inference pipelines
•Python scripting and PowerShell skills
Nice to Have
•Palantir Foundry DQC setup, build failure and data pipeline diagnostics
•Monte Carlo data quality alert management and observability
•Azure DevOps repository management
•Investment management data and domain systems such as Optima and Cardia
•Data lineage and metadata-driven operations
•Enterprise-scale data platforms
•Batch orchestration and data workflow tools
Conditions
No conditions are specified in the posting.
