You will own the stability, availabilityand performance of our production platforms. This is a hands-on lead/managerrole that combines production support, DevOps and site reliability engineering(SRE). You will lead engineers, run major incidents, and read the code to findroot causes. You will also use AI tools to cut toil and resolve incidentsfaster.

What we expect of you

Accountable. You keep an eye on everything. Every productionservice, alert, incident and risk in your scope is yours to know about, andnothing should surprise you.

Responsible. The buck stops with you. When production is atrisk, you know what to do, you decide, and you see it through to resolution.

Leadership. You own your team. You set the standard foron-call, coach engineers, back them during incidents, and answer for theirresults.

Technical. You know your stuff. You go deep on theplatform, the data stores, the pipelines and the code, and your team trustsyour judgment because you have done the work.

What you will do

Production support and reliability

‒Lead the production supportfunction, including on-call rotations, escalation paths and 24x7 coverage.

‒Command major incidents fromdetection through resolution, then run blameless post-incident reviews anddrive the fixes to completion.

‒Define and track service levelindicators (SLIs), service level objectives (SLOs) and error budgets withengineering and product teams.

‒Use Datadog to buildmonitoring, alerting and dashboards that catch problems before users do.

‒Run and improve Kubernetesworkloads in the cloud, including scaling, upgrades, capacity and cost.

‒Support and tune PostgreSQL,MongoDB and Neo4j in production, including performance, backup, recovery andfailover.

‒Keep Confluent Kafka healthy:throughput, consumer lag, partitioning, retention and schema management.

‒Strengthen CI/CD pipelines,release practices, rollback plans and infrastructure as code.

‒Report on availability,incident trends, mean time to detect (MTTD) and mean time to resolve (MTTR) tosenior leaders.

‒Hire, coach and develop ahigh-performing team.

Incident and problem management

‒Own the end-to-end incidentmanagement process: detection, triage, severity classification, escalation,communication, resolution and closure.

‒Act as incident commander forSev 1 and Sev 2 events, coordinating engineering, vendors and businessstakeholders, and issuing clear status updates to leadership at set intervals.

‒Own problem management: runroot cause analysis (RCA), maintain the known error database, and trackcorrective and preventive actions to closure.

‒Identify recurring incidentsthrough trend analysis, and prioritize permanent fixes ahead of repeatfirefighting.

‒Set and enforce service levelagreements (SLAs) for response and resolution, and report breaches with causesand remedies.

‒Run change and release riskreviews with the change advisory board (CAB), and link incidents back to thechanges that caused them.

‒Maintain on-call runbooks,escalation matrices and major incident playbooks, and test them through regularincident drills.

‒Run weekly and monthlyincident, problem and service health reviews with engineering, product andbusiness owners.

‒Manage third-party and vendorescalations for cloud, platform and software issues, and hold vendors to theirsupport commitments.

‒Keep the incident and problemtooling (for example ServiceNow or Jira) accurate, and ensure tickets carrycomplete timelines, impact and root cause.

Quality, CSAT and team performance

‒Design and run a customersatisfaction (CSAT) process for all production support resources, with a surveyafter ticket closure,



a defined target score and a review of every low score.

‒Gather CSAT from businessusers, application owners and engineering partners, and turn the feedback intocoaching and process fixes.

‒Create quality gates for tickethandling: accurate categorization and priority, complete root cause andresolution notes, correct escalation, and knowledge article updates beforeclosure.

‒Audit a sample of closedtickets each month against the quality gates, score each engineer and team, andshare results openly.

‒Create release and handovergates, such as production readiness, runbook and monitoring sign-off, before aservice moves to support.

‒Measure quantitativeperformance for each support team and engineer: ticket volume, backlog andaging, first response time, time to resolve, SLA attainment, reopen rate,first-contact resolution and escalation rate.

‒Build dashboards that combinequality and quantity metrics, so no one is rewarded for closing tickets fast atthe cost of quality.

‒Set targets and thresholds foreach metric, run corrective plans where teams miss them, and recognize teamsthat exceed them.

‒Use the data in performancereviews, capacity planning and staffing decisions, and report trends to seniorleaders every month.

Hands-on technical work

‒Troubleshoot live productionissues hands-on: pod crashes, node pressure, network policies, DNS, ingress,certificates and resource limits in Kubernetes.

‒Analyze application logs,distributed traces, thread dumps, heap dumps and metrics to isolate root causeacross microservices.

‒Review code changes,configuration and release diffs to find the commit, dependency or settingbehind a regression.

‒Tune PostgreSQL queries,indexes, connection pools, vacuum and replication. Diagnose MongoDB slowoperations, sharding and replica set issues, and Neo4j query plans and memory.

‒Monitor and resolve Kafkaissues: consumer lag, rebalancing, partition skew, broker health, connectorfailures and schema compatibility.

‒Build and maintaininfrastructure as code (Terraform) and Helm charts, and review changes made bythe team.

‒Write automation in Python,Bash or PowerShell for remediation, health checks and routine operations.

‒Troubleshoot CI/CD pipelinefailures, build and deployment issues, and container image and registryproblems.

‒Run load, failover and chaostests to validate resilience, and fix the gaps they expose.

‒Manage identity, secrets,certificates and network access in the cloud, working with security teams onvulnerabilities and patching.

‒Tune cloud and clustercapacity, autoscaling and cost.

‒Write and keep currentrunbooks, architecture diagrams and dependency maps for production services.

‒Use Copilot and other AI toolsto speed up log analysis, query tuning, script writing and incident summaries,and validate every output before it touches production.

Architecture and design

‒Review solution architectureand design documents for recent and changing services, and flag reliability,scalability, security and operability risks before release.

‒Map service dependencies, dataflows and failure domains across the microservices estate. Use that view to setresilience patterns such as retries, circuit breakers, timeouts and gracefuldegradation.





‒Partner with architects andengineering leads on platform design decisions, including multi-region anddisaster recovery strategy, data store selection, and event-driven patterns onKafka.

‒Set production readinessstandards, and gate releases on them: observability, SLOs, runbooks, rollbackplans and capacity.

Code analysis

‒Read and analyze applicationcode to understand behavior in production, including concurrency, memory use,query patterns, error handling and retry logic.

‒Review pull requests foroperational risk, such as missing logging, unbounded queries, weak errorhandling and unsafe configuration.

‒Trace recurring incidents backto design or code defects, and work with development teams to fix thempermanently.

‒Use static analysis, dependencyscanning and code quality tools to find reliability and security risks early.

Platform engineering and AI-driven automation

‒Build and improve internalplatform capabilities such as self-service environments, standard pipelinetemplates, golden paths and reusable Helm and infrastructure modules.

‒Use AI tools, including GitHubCopilot, to generate and review automation scripts, pipeline code,infrastructure as code and runbooks, then test and validate before release.

‒Apply AI to operationalworkflows: alert triage, log and trace summarization, incident timelines,post-incident report drafts and ticket classification.

‒Automate remediation of knownfailure patterns through self-healing scripts, event-driven workflows andauto-scaling policies.

‒Measure toil, set automationtargets, and report time saved, incident reduction and MTTR improvement from AIand automation work.

‒Define guardrails for AI use inproduction, including access controls, review requirements and audit trails.

What you bring

Required

‒10+ years in productionsupport, DevOps or SRE, including at least three years leading teams.

‒Career progression fromon-premises infrastructure or systems administration into cloud administration,DevOps and SRE, and then into technical leadership. You understand the fullstack from hardware, networking and operating systems up to cloud-native services,and you use that depth to troubleshoot.

‒Deep hands-on experience with amajor cloud platform, Azure or AWS (compute, networking, identity, monitoring,resilience).

‒Production-level Kubernetesexpertise on a managed service such as Azure Kubernetes Service (AKS) or AmazonElastic Kubernetes Service (EKS), including troubleshooting, Helm anddeployment strategies.

‒Working knowledge ofPostgreSQL, MongoDB and Neo4j operations.

‒Experience running ConfluentKafka in production.

‒Strong grounding inmicroservices architecture, including service dependencies, API failures anddistributed tracing.

‒Proven use of Datadog orcomparable observability platforms.

‒CI/CD experience with toolssuch as Azure DevOps, GitHub Actions or Jenkins.

‒Ability to read and debug codein at least one major language, for example Java, C# or Python.

‒Track record managinghigh-severity incidents and communicating clearly with technical and businessaudiences.

‒Practical experience applyingAI tools, such as Copilot, in engineering or operations work.

Preferred

‒Terraform or Bicep experience.

‒ITIL or similar servicemanagement practices.

‒Azure, AWS, Kubernetes or SREcertifications.

‒Experience in a regulated orlarge-scale enterprise environment.

How you will be measured

Availability against SLOs, MTTD and MTTR,repeat-incident rate, CSAT scores, ticket quality gate results, SLA attainment,on-call health, automation gains, and the quality of post-incidentfollow-through.

📌 Production Application Support Lead (Hyderabad)
🏢 Algoleap Technologies
📍 Hyderabad