Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers.

Scope of the Role:

We are looking for a hands-on Application Support Engineer to support, maintain, and enhance business-critical enterprise applications built and running on Google App Engine (GAE) and microservices.

The role is focused on application availability, production support, incident response, troubleshooting, and continuous feature enhancement rather than building a new application from the ground up. The ideal candidate can quickly understand an existing microservices-based application landscape, restore service when users are impacted, and deliver incremental enhancements safely across test, pre-production, and production environments.

Experience supporting large-scale, business-critical applications in complex enterprise technology environments is preferred.

What You’ll Own:

Provide production support and maintenance for enterprise applications hosted on Google App Engine, including standard and flexible environments.
Act as a first point of contact for user-impacting incidents: triage, diagnose, restore service, and drive issues to closure within agreed SLAs/SLOs.
Troubleshoot application errors, failed requests, latency and performance degradation, service-to-service failures, configuration issues, quota/scaling limits, and dependency or integration failures.
Design, develop, and deliver feature enhancements and functional improvements to existing applications based on user and business needs.
Support applications built on microservices architecture, including service boundaries, APIs/contracts, inter-service communication, authentication, and failure/retry behavior.
Own build, release, and deployment activities across development, test, pre-production, and production environments with appropriate validation, approvals, and rollback plans.
Manage App Engine deployments including versions, traffic splitting/migration, canary and staged rollouts, rollbacks, service configuration, and scaling settings.
Perform root-cause analysis for recurring production issues and implement sustainable fixes.
Build and maintain monitoring, alerting, logging, dashboards, and error reporting using Cloud Monitoring, Cloud Logging, Error Reporting, and Cloud Trace.
Support platform, framework, library, dependency, and runtime upgrades while maintaining stability, supportability, and compliance.
Support IAM, service accounts, access controls, secrets management, and operational governance.
Participate in change management, release-readiness reviews, and on-call/rotational support as required.
Collaborate with client and cross-functional Application Engineering, Product, QA, Data Engineering, Infrastructure, and Platform teams.
Adapt to established client-specific engineering, security, review, change-management, and operational processes.
Create and maintain technical documentation, operational runbooks, troubleshooting guides, deployment procedures, and support playbooks.

Microservices Expectations:

Service decomposition and ownership: understand service boundaries, upstream/downstream dependencies, and ownership of behavior or data.
APIs and contracts: REST and gRPC/protobuf interfaces, versioning, backward compatibility, and contract-change impacts.
Inter-service communication: synchronous calls, asynchronous/event-driven messaging (Pub/Sub, Cloud Tasks), idempotency, retries, timeouts, backoff, and circuit breaking.
Service-to-service authentication and authorization using service accounts, identity/tokens, and least-privilege access.
Distributed troubleshooting using logs, distributed tracing, and correlation IDs to isolate failures across services.
Failure modes at scale, including cascading failures, partial outages, hot spots, quota exhaustion, and graceful degradation.
Independent deployability and coordination of multi-service releases when required.
Service-level observability, including dashboards, SLIs/SLOs, alerting, and error budgets.

You’ll Thrive in This Role If You Have:

3-7 years of experience in Application Support, Application Engineering, Software Engineering, Cloud Engineering, or a related role.
Strong hands-on experience supporting, maintaining, and enhancing production applications on Google Cloud Platform (GCP).
Hands-on experience with Google App Engine, including deployment, configuration, scaling, versioning, and troubleshooting.
Solid understanding of microservices architecture, REST/gRPC APIs, service-to-service communication and authentication, distributed tracing, and cross-service debugging.
Practical experience with deployments and promotions across multiple environments, including release validation, rollback, and change control.
Strong programming skills in one or more of Python, Java, Node.js / JavaScript, or Go.
Working knowledge of SQL and application data stores such as Cloud SQL, Firestore/Datastore, Cloud Spanner, or BigQuery.
Good understanding of GCP IAM, service accounts, permissions, monitoring, logging, alerting, and production operations.
Experience with CI/CD pipelines and automated build/deployment tooling such as Cloud Build, Jenkins, GitHub Actions, or GitLab CI.
Experience troubleshooting complex production environments and performing root-cause analysis under time pressure.
Ability to quickly understand existing systems, codebases, services, configurations, and client-specific tools and workflows.
Strong communication and collaboration skills, including communication during user-impacting incidents.

Preferred Qualifications:

Experience supporting large-scale internal or enterprise applications with demanding availability, reliability, and performance requirements.
Ability to quickly learn and operate within enterprise-specific application frameworks, deployment tooling, and support processes.
Cloud Run, GKE, Cloud Functions, or other GCP application services.
Apigee / API Gateway, load balancing, and API management.
Pub/Sub, Cloud Tasks, Cloud Scheduler, and asynchronous/event-driven patterns.
Infrastructure as Code, particularly Terraform.
SRE practices including SLIs/SLOs, error budgets, incident management, and blameless postmortems.
Containerization with Docker and Kubernetes fundamentals.
Frontend or full-stack experience supporting user-facing web applications.
Looker / Tableau / BI platforms or reporting integrations.
Supporting AI/ML, GenAI, or LLM-based applications running on GCP, including Vertex AI.

The expected salary range for this position is $60,000 - $110,000 USD per year, based on experience, skills, and qualifications.

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