Devops Lead

If you feel your profile suits this role please share the below details to (varskumari@deloitte.com)

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Platform & cloud architecture Build and extend the cloud-native platform and golden paths. Design scalable, highly available, fault-tolerant solutions across AWS / Azure / GCP, delivered as infrastructure-as-code and guardrails, within the architecture set for the pod.

Cloud migration delivery — Lead workload migrations end to end: discovery and wave planning, landing zones as code, automated cutover and rollback, then modernization, decommissioning, and cost optimization across on-prem, cloud-to-cloud, and hybrid workloads.

CI/CD & containers — Build and operate the delivery backbone: CI/CD pipelines (Jenkins, GitHub Actions, GitLab CI, Azure DevOps), GitOps-driven deployment (ArgoCD/Flux), and progressive delivery. Run containerized workloads at scale on Kubernetes (EKS/AKS/GKE) with Helm and service mesh.

Observability & monitoring — Instrument services with metrics, logs, and traces using OpenTelemetry (Prometheus/Grafana, ELK/EFK, Datadog, Sumo Logic), with actionable alerting and dashboards so your squad can see and debug what they ship. ---Internal Use---

DevSecOps — Embed shift-left scanning into CI/CD (SAST, DAST, SCA, IaC, container, and secrets scanning), implement identity and access management, secrets management, and encryption, and drive risk-based vulnerability management for your workstream.

Data & ML pipelines — Deliver large-scale, zero-downtime data migrations and the pipelines behind them — ETL/ELT (e.g., Airflow, dbt) and lakehouse infrastructure (e.g., Iceberg) as code — and support MLOps trainvalidatedeployretrain pipelines (e.g., Kubeflow, MLflow) with canary/shadow serving and production monitoring for drift, accuracy, latency, and cost.

AI-assisted DevOps — Build and use agentic DevOps tooling: MCP servers and agents that handle deployments, incident triage, root-cause analysis, and self-healing remediation — with permissioned access, human-in-the-loop guardrails, LLM evals, and token/cost monitoring.

Squad leadership & mentoring — Lead a small squad: plan and review work, set the bar for technical rigor and automation, mentor mid and junior engineers, and represent your workstream to the client and the wider pod.

What we’re looking for

4+ years — architecting and operating complex, scalable, secure, fault-tolerant systems on AWS / Azure / GCP, including time leading a workstream or squad.

Deep hands-on DevOps engineering — CI/CD, infrastructure-as-code, Kubernetes, cloud automation, and the platform tooling and agents on top. Strong in Python (preferred), plus Go and/or Bash.

Proven migration experience — you’ve planned and led real cloud workload migrations (landing zones, wave planning, automated cutover) and/or large-scale data migrations, not just greenfield builds.

AI-native DevOps mindset — you build agentic DevOps tooling, use AI to accelerate engineering, and have run MLOps in production. AI-first thinking is the baseline, not a differentiator.

DevSecOps fluency — you’ve run threat models, stood up scanning and vulnerability management, and worked within SOC 2 / HIPAA or similar frameworks.

A platform mindset — a track record of building reusable patterns (paved roads, golden paths, self-serve guardrails), not one-off work that doesn’t scale.

Team leadership — you’ve led and mentored small squads, reviewed others’ work, and set technical direction for a workstream.

Strong communication — clear written and verbal communication, able to explain complex technical topics to any audience and align stakeholders across teams.

Education — B.E / B.Tech, MCA, or M.E / M.Tech

Bonus points

Professional-level cloud certifications (AWS/Azure/GCP), CKA/CKAD, and security/AI certs