Senior Application Support Engineer – AI Products & Agents

As a Senior Application Support Engineer – Operations, Reliability & AI Platforms, you will provide advanced technical support for a suite of products, acting as the escalation point for L2 support and customer queries. You will troubleshoot, diagnose, and resolve incidents related to application functionality, integrations, performance, and security, collaborating closely with development and cross-functional teams to deliver timely fixes and ensure product quality.

Responsibilities

Provide advanced technical support for a suite of products, acting as the escalation point for L2 support and customer queries; Troubleshoot, diagnose, and resolve incidents related to application functionality, integrations, performance, and security; Collaborate with development teams (frontend, backend, full stack) to analyse root causes, reproduce issues, and deliver timely fixes or workarounds; Monitor application health, logs, and alerts using tools such as AWS CloudWatch, Winston, or similar; Maintain and update knowledge base articles, FAQs, and runbooks for recurring issues and resolutions; Support deployment activities, including post-release validation and rollback procedures; Participate in incident management, including RCA, documentation, and communication with stakeholders; Ensure compliance with internal standards and external regulations (e.g., GDPR, HIPAA, as relevant); Contribute to continuous improvement of support processes; Collaborate cross-functionally with Product, QA, Engineering, Platform, and Operations teams; Support cloud-native and distributed application environments across AWS, Azure, and GCP platforms; Drive operational excellence through automation, monitoring, observability, and reliability engineering practices; Support AI-powered applications, Generative AI solutions, chatbots, copilots, and agent-based systems running in production environments; Troubleshoot and resolve incidents related to AI platforms, LLM integrations, MCP servers, vector databases, retrieval services, and AI orchestration workflows; Support deployment, monitoring, performance tuning, and operational stability of AI-enabled applications and services; Participate in operational readiness reviews, disaster recovery planning, capacity management, and production support activities for AI workloads

Requirements

4-6 years of experience in application support, technical troubleshooting, DevOps, Site Reliability Engineering (SRE), or software engineering; Experience supporting products in regulated industries (Life Sciences, Healthcare, Financial Services, etc.); Familiarity with frontend frameworks (React, Material-UI) and backend technologies (Node.js, Express.js); Knowledge of security best practices and compliance requirements; Exposure to Agile methodologies and SDLC; Experience with knowledge base management and documentation; Interest in continuous learning and process improvement; Bachelor's degree in Computer Science, Engineering, or related field (or equivalent practical experience); Strong understanding of CI/CD pipelines and deployment automation tools such as GitHub Actions, Azure DevOps, Jenkins, GitLab CI/CD, or similar; Experience with Infrastructure as Code (IaC) tools such as Terraform, CloudFormation, or Bicep; Understanding of containerization and orchestration technologies such as Docker and Kubernetes; Knowledge of application monitoring, observability, alerting, logging, and troubleshooting methodologies; Experience supporting production systems in cloud environments; Familiarity with release management, change management, incident management, and problem management processes; Knowledge of high availability, disaster recovery, backup, failover, and resilience engineering concepts; Working knowledge of Generative AI and Large Language Model (LLM) ecosystems; Understanding of AI architecture patterns (LLM-based Applications, Retrieval-Augmented Generation (RAG), AI Agents and Multi-Agent Systems, Prompt Engineering, Vector Databases and Semantic Search, AI Service Integration Patterns, Agentic Workflows and Orchestration Frameworks); Familiarity with Model Context Protocol (MCP), MCP Server architecture, MCP Clients, Tool Calling, and Enterprise AI integrations; Understanding of AI observability, monitoring, tracing, evaluation, and operational support practices; Exposure to Azure OpenAI, AWS Bedrock, Google Vertex AI, Azure AI Services, OpenAI APIs, Anthropic Claude, or similar AI platforms; Knowledge of AI governance, responsible AI practices, security, compliance, and operational risk management; Strong analytical and problem-solving skills with hands-on experience debugging web applications (frontend and backend); Experience with monitoring, logging, and alerting tools (e.g., AWS CloudWatch, Winston, Datadog, ELK, Splunk, Grafana, Prometheus); Experience with ticketing systems (e.g., Jira, ServiceNow) and incident management workflows; Good understanding of RESTful APIs, databases (PostgreSQL, Redis), and cloud platforms (AWS, Azure, GCP); Use of scripting skills (e.g., Bash, Python, JavaScript, PowerShell) for automation and troubleshooting; Availability to work flexible hours, non-standard hours, weekends and holidays, and different shifts (6AM to 2PM and 2PM to 11PM) when required

Nice to have

Experience with SRE principles and operational excellence frameworks; Experience implementing self-healing and automated remediation workflows; Experience supporting Kubernetes-based applications and cloud-native architectures; Familiarity with LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, or similar AI orchestration frameworks; Experience supporting MCP-based enterprise integrations and AI tool ecosystems; Understanding of AI platform operations, AI FinOps, token management, model monitoring, and cost optimization; Experience supporting enterprise-scale observability platforms and AIOps solutions; Cloud certifications (AWS, Azure, GCP), DevOps certifications, or AI/Generative AI certifications; Experience working in 24x7 support, on-call, or mission-critical production environments