Cloud & DevOps

Google Cloud Platform Training in Pune with Placement

Name: Archer Infotech
Address: Flat No. 12, Divyadarshan Housing Society, behind Kothrud Bus Stand Road, near Natraj Gas Agency, Londhe Wada, Chaitanya Nagar, Kothrud, Pune, Maharashtra, 411038, IN
Telephone: +91 9850 678451
Price range: ₹₹

Pune's trusted GCP classes at the Archer Infotech institute, Kothrud — weekday, weekend and online batches with placement assistance.

Master Google Cloud Platform services. Learn compute, storage, data services, and prepare for GCP certifications.

2.5 Months

Intermediate

Online & Offline

Curriculum last reviewed: 6 May 2026

Interested in this course?

Get in touch with us to learn more about the curriculum, batch timings, and fees.

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Google Cloud Platform (GCP) is the third major cloud in Pune — significantly smaller than AWS and Azure but distinguished by leadership in data analytics (BigQuery), Kubernetes (GKE — Google invented Kubernetes), and AI / ML (Vertex AI plus Gemini exclusivity). Pune teams at Tiger Analytics, Fractal Analytics, ZS Associates, MathCo, plus the data-heavy product engineering arms (Persistent Data Engineering practice, Mastercard Pune Tech Hub for some workloads, BMW TechWorks for ADAS data pipelines) run substantial GCP workloads. Archer Infotech's Google Cloud training in Pune teaches the platform as it is actually used in 2026 — Compute Engine, GKE (with Autopilot mode), Cloud Run for serverless containers, BigQuery for data analytics, Cloud Functions, Vertex AI for ML / GenAI, plus IaC via Terraform and the gcloud CLI. Classroom in Kothrud, online live, and weekend batches available.

Why Learn Google Cloud in 2026

GCP holds roughly 11% of the global cloud infrastructure market (Synergy Research, Q1 2026) — third behind AWS (31%) and Azure (25%) — but its share is concentrated in data / ML / AI workloads where it leads. In Pune specifically, GCP is the dominant cloud at most analytics-heavy companies (Tiger Analytics, Fractal Analytics, ZS Associates, MathCo) and an increasing presence at AI-platform startups, plus several BMW TechWorks autonomous-driving data pipelines. Indeed Pune lists more than 500 active GCP-related roles as of May 2026, smaller than AWS / Azure but with stronger compensation per role because the talent supply is thinner.

What changed in 2026: GKE Autopilot mode has matured into the default for new Kubernetes workloads (managed control + node autoscaling, lower operational overhead). Cloud Run has expanded beyond stateless HTTP to support background jobs and longer execution times. Vertex AI has consolidated Google's ML / GenAI offering — model garden, model registry, plus exclusive Gemini access (the Anthropic / OpenAI alternative for enterprise teams that want to ship Google's frontier model). BigQuery's BigLake pattern (querying lake-house data without ingestion) has become the default for analytics-heavy teams. Terraform 1.7+ with the google provider remains dominant for IaC.

What this means for hiring: 2026 Pune GCP JDs expect Compute / GKE / Cloud Run fluency, BigQuery for analytics teams, IAM / VPC fundamentals, IaC via Terraform, plus at least one observability story (Cloud Operations Suite — Logging, Monitoring, Trace). Senior roles add Vertex AI, BigQuery optimisation, and multi-cluster / multi-region patterns. Archer Infotech's curriculum is rebuilt around exactly these expectations — modern GCP, IaC by default, data + AI aware.

500+ active GCP roles on Indeed Pune as of May 2026 — thinner supply, stronger compensation per role
Pune analytics ecosystem — Tiger / Fractal / ZS / MathCo all run substantial GCP
GCP leads on data (BigQuery), Kubernetes (GKE — Google invented K8s), and AI (Vertex AI + Gemini)
GKE Autopilot + Cloud Run + BigQuery + Vertex AI — the modern GCP stack
Certification path — Associate Cloud Engineer (covered in our follow-on track)

Who This Course Is For

For You If

Working developer or data engineer at a Pune analytics company (Tiger / Fractal / ZS / MathCo) where GCP is the institutional default
AWS or Azure cloud engineer wanting to add GCP for multi-cloud reach
Engineering / BCS / MCA student targeting analytics-engineering roles in Pune where GCP is dominant
Working data engineer wanting BigQuery + Vertex AI depth for senior analytics roles
Working ML engineer targeting Pune AI-platform startups that run on GCP
Career restarter targeting cloud engineering at analytics-heavy companies

Not For You If

If you have no programming or scripting background — at least basic Python is required
If your goal is Pune captives / .NET / Microsoft ecosystem — Azure is the right choice; GCP adoption is minimal there
If your goal is Pune product engineering / SaaS / fintech without analytics emphasis — AWS is wider
If you cannot put in 8–10 hours per week of lab work outside class — cloud is learned by clicking, breaking, rebuilding
If you only want a single certificate sticker — talk to us about the focused GCP Associate Cloud Engineer track

Detailed Curriculum

GCP Foundations & Account Setup

Week 1

Cloud computing concepts, the GCP global infrastructure (Regions, Zones, Network Edge Locations), the management hierarchy (Organisation → Folders → Projects → Resources), project / billing setup, plus the gcloud CLI essentials. Set up a personal sandbox project with billing alarms set at ₹1,000, an IAM principal with appropriate roles, and the Cloud SDK locally. We deliberately spend a full session on cost — every horror story you have heard about a runaway GCP bill starts with what we cover this week.

Cloud computing models — IaaS / PaaS / SaaSGCP global infrastructure — Regions, Zones, EdgeOrganisation, Folders, Projects, ResourcesProject setup, billing, free trial creditsBudget alerts and cost controlsgcloud CLI and Cloud ConsoleCloud Shell for browser-based admin

IAM, VPC & Networking

Week 2

Identity is the foundation of cloud security. Cover IAM principals (users, service accounts, groups), roles (basic, predefined, custom), the resource-hierarchy IAM inheritance model (different from AWS / Azure — important to internalise), Workload Identity Federation for non-GCP CI/CD pipelines. Then networking — VPC (default vs custom mode), subnets across regions, firewall rules, Cloud NAT, Cloud Load Balancing (the global vs regional distinction), VPC Peering and Shared VPC, plus Cloud DNS.

IAM principals and rolesResource-hierarchy IAM inheritanceService Accounts and Workload Identity FederationVPC — auto vs custom mode subnetsFirewall rules and network tagsCloud NAT and Cloud RouterCloud Load Balancing — global vs regionalVPC Peering, Shared VPCCloud DNS and private DNS

Compute — Compute Engine, GKE, Cloud Run, Functions

Weeks 3–4

The compute landscape on GCP in 2026. Compute Engine (VMs) — instance families, custom machine types (a GCP differentiator), preemptible / spot instances, Managed Instance Groups + Autoscaling. Then containers — GKE with Autopilot mode (the 2026 default for new clusters), Standard mode for advanced control, plus Cloud Run for serverless containers (the right choice for many web services). Then Cloud Functions for event-driven compute, Cloud Run Jobs for batch work, plus the App Engine option (legacy but still used).

Compute Engine — families, custom machine typesPreemptible / spot instancesManaged Instance Groups and AutoscalingGKE Autopilot — the 2026 defaultGKE Standard mode for advanced controlCloud Run for serverless containersCloud Run Jobs for batch workloadsCloud FunctionsContainer Registry / Artifact Registry

Storage, Databases & BigQuery

Weeks 5–6

Storage and data services where GCP differentiates most. Cloud Storage (object storage with classes — Standard, Nearline, Coldline, Archive), Filestore for shared filesystems, Persistent Disks for VMs. Databases — Cloud SQL (managed PostgreSQL / MySQL / SQL Server), Cloud Spanner for globally distributed strong consistency (the unique GCP offering), Firestore for document NoSQL, Memorystore for Redis. Then the GCP differentiator — BigQuery — the serverless petabyte-scale analytics warehouse that is the reason most Pune analytics teams are on GCP. BigLake for federated queries over Cloud Storage data, BigQuery ML for in-database machine learning, plus query optimisation patterns.

Cloud Storage — Standard, Nearline, Coldline, ArchiveFilestore and Persistent DisksCloud SQL — PostgreSQL, MySQL, SQL ServerCloud Spanner basicsFirestore for document NoSQLMemorystore for RedisBigQuery — partitions, clustering, query optimisationBigLake for federated queriesBigQuery ML basics

DevOps on GCP — Terraform, Cloud Build, Observability

Weeks 7–8

Modern GCP is code, not clicks. Cover Terraform 1.7+ with the google provider — providers, state management (state in GCS with state locking), modules, plus the Cloud Foundation Toolkit (Google's published Terraform modules). Then CI/CD — Cloud Build (the GCP-native option) and GitHub Actions with Workload Identity Federation (no static keys). Cover the Cloud Operations Suite — Cloud Logging, Cloud Monitoring with PromQL-compatible queries, Cloud Trace, plus the GKE-native option of running Prometheus + Grafana via Google Cloud Managed Service for Prometheus.

Terraform 1.7+ with google providerState in GCS with lockingCloud Foundation Toolkit modulesCloud Build — triggers, builds, deploymentsGitHub Actions with Workload Identity FederationCloud Logging and log-based metricsCloud Monitoring with PromQLCloud Trace and OpenTelemetryManaged Service for Prometheus

Vertex AI & GenAI on GCP

Week 9

GCP's AI / ML platform consolidated. Cover Vertex AI workbench (managed Jupyter for data scientists), Vertex AI Pipelines for ML workflow orchestration, the model registry, plus the Model Garden for pre-trained models (Gemini, Claude on GCP marketplace, open-source models). Vertex AI Search for managed RAG, Vertex AI Agent Builder for tool-using assistants, plus Gemini 2.5 Pro on Vertex AI for the frontier-model layer. Build a small RAG service against a real corpus that demos in 5 minutes.

Vertex AI Workbench (managed Jupyter)Vertex AI PipelinesModel Registry and servingModel Garden — Gemini, open-source modelsVertex AI Search for managed RAGVertex AI Agent BuilderGemini 2.5 Pro on Vertex AI

Capstone Project & Interview Preparation

Week 10

Two weeks of full-time capstone work plus structured interview preparation. Pick one of three capstone projects (see Capstone Projects). Mock interviews calibrated for Pune GCP hiring panels — Tiger Analytics, Fractal, ZS, MathCo, Persistent Data Engineering. Includes a BigQuery query-optimisation mock round, an architecture / scenario round, and a behavioural round.

Capstone implementation, deployment, READMEBigQuery query-optimisation mock roundGCP architecture / scenario mock roundResume + LinkedIn rewriteGitHub portfolio polishHR mock interview and salary negotiation

Capstone Projects You Will Build

Project 1: Three-Tier Architecture with Terraform on GCP

A complete production-style three-tier architecture provisioned by Terraform — VPC with public / private subnets, Cloud Load Balancer, GKE Autopilot or Compute Engine Managed Instance Group, Cloud SQL PostgreSQL with HA, Memorystore for caching, Cloud Storage + Cloud CDN for static assets. Outcome: a public GitHub repository plus an architecture diagram you can talk through in any cloud interview.

Terraform 1.7+GKE Autopilot or Compute Engine MIGCloud SQL PostgreSQL HAMemorystore (Redis)Cloud Load Balancing + Cloud CDNCloud Operations SuiteGitHub Actions with Workload Identity Federation

Project 2: Data Analytics Pipeline with BigQuery + Cloud Composer

An end-to-end analytics pipeline — ingest data from multiple sources to Cloud Storage, schedule processing with Cloud Composer (managed Airflow), transform with dbt or Dataform, load into BigQuery with proper partitioning and clustering, build a Looker Studio dashboard. Demonstrates the patterns Pune analytics teams (Tiger / Fractal / ZS / MathCo) test for.

Cloud Storage + BigQueryCloud Composer (Airflow) or Dataformdbt for transformationsLooker Studio dashboardTerraform IaC

Project 3: Vertex AI RAG Service with Gemini

A 2026-relevant AI capstone — Cloud Storage PDFs ingested, embeddings stored in Vertex AI Vector Search, Vertex AI Agent Builder powering a domain assistant via Gemini 2.5 Pro, served via Cloud Run with streaming responses. Includes evaluation via Vertex AI Evaluation Service.

Vertex AI Workbench + Vector SearchVertex AI Agent BuilderGemini 2.5 Pro on Vertex AICloud Run for servingCloud Storage + Cloud SQL

Career Outcomes & Salaries in Pune

GCP Cloud Engineer is among the most-niche-but-well-paid cloud roles in Pune in 2026 — Indeed Pune lists 500+ active openings, smaller than AWS / Azure but with stronger compensation per role because the talent supply is thinner. The biggest Pune employers are Tiger Analytics, Fractal Analytics, ZS Associates, MathCo, Persistent Data Engineering, Mastercard Pune Tech Hub (for some workloads), plus BMW TechWorks autonomous-driving data pipelines.

What pulls a GCP cloud engineer above the median band: a public GitHub portfolio with at least one Terraform-deployed three-tier architecture on GCP, demonstrable BigQuery optimisation experience (the GCP differentiator), one Vertex AI / Gemini integration project, and the Associate Cloud Engineer or Professional Cloud Architect certificate. Most students take the focused GCP Associate Cloud Engineer track after this course as the certification specialisation.

Senior Cloud Architect bands at the top end are reported as national figures (Pune-specific Indeed pages do not exist for these specific titles); Pune trends within ±10% of these figures based on AmbitionBox and 6figr.

Role	Salary band	Source
GCP Cloud Engineer (Pune)	₹7,80,000 per year average	Indeed Pune (GCP Cloud Engineer)
Cloud Engineer entry-level (<3 years, Pune)	₹5,00,000 – ₹8,00,000 per year	AmbitionBox Pune Cloud Engineer
GCP Solutions Architect (Pune mid-level, 3–6 years)	₹14,00,000 – ₹22,00,000 per year	Glassdoor Pune GCP Architect
Senior GCP Architect / Data Engineer (national, 7+ years)	₹26,00,000 – ₹45,00,000 per year	6figr India Senior GCP Architect (Pune ±10%)

Pune companies hiring GCP professionals in 2026

Tiger AnalyticsFractal AnalyticsZS AssociatesMathCoPersistent Systems (Data Engineering)Mastercard Pune Tech HubBMW TechWorks IndiaCognizantCapgeminiTCSInfosysAtos / Eviden

Roles after this GCP course

GCP Cloud EngineerCloud Data EngineerDevOps Engineer (GCP-focused)Junior Solutions ArchitectAnalytics Engineer (with BigQuery depth)ML Engineer (with Vertex AI)

Course Duration, Batches & Modes

Duration: 10 weeks of structured curriculum plus 2 weeks of capstone project and interview preparation (~2.5 months total)

Classroom

Archer Infotech, Kothrud, Pune

• Morning batch — 10:00 to 13:00
• Evening batch — 18:00 to 21:00
• Lab access available outside class hours

Online Live

• Same hours as classroom batches
• Recordings available for review
• Same lab reviews as in-person batches

Tools used:

Zoom for live sessionsPersonal GCP sandbox per student (free trial credits)GitHub for code and Terraform reviewsSlack / WhatsApp for async Q&A

Weekend

• Saturday + Sunday, 09:00 to 13:00

Stretches over ~4 months instead of 2.5 to accommodate working professionals.

Maximum 15 students per batch. Classroom batches start every 4 weeks; weekend batches every 6 weeks.

Course Fees

Course fees range from ₹20,000 to ₹90,000 depending on mode and concession. GCP Free Tier + the $300 free trial credit cover the lab work for most students.

₹20,000 – ₹90,000

Payment options:

Single payment with early-bird discount
EMI in 2–3 instalments at no extra cost
Corporate sponsorship — invoiced to your employer with GST

Placement Support

Placement support starts from week 8 of the course. By the time you finish the curriculum, your resume highlights real Terraform on GCP work, your GitHub has a deployable three-tier reference architecture, and you have completed at least three mock technical interviews against question banks from Pune GCP hiring teams.

We say placement support, not placement guarantee. Our support is unconditional, time-bound (six months after course completion), and includes free re-entry to a future batch's interview-prep sessions if your first round of interviews does not land.

Placement process — week by week

Week 8 — resume and LinkedIn rewrite for GCP cloud-engineer JDs
Week 9 — GitHub portfolio cleanup, Terraform README polish
Weeks 10–11 — three rounds of mock technical interviews
Week 11 — HR mock interview and salary negotiation coaching
Post-course — referrals via our 17-year alumni network at 12+ partner companies (with extra emphasis on Pune analytics scene)
Up to 6 months of continued support after course end
Free re-entry to future batch interview-prep sessions if first round does not land

Partner companies

Tiger AnalyticsFractal AnalyticsZS AssociatesMathCoPersistent SystemsMastercard Pune Tech HubBMW TechWorks IndiaCognizantCapgeminiTCSInfosys

See recent placement records →

How Archer Infotech Compares

We compare ourselves against typical Pune GCP training institutes on factual rows only — no logos, no opinions.

Factor	Archer Infotech	Typical Pune institute
Trainers named on course page with photos and LinkedIn	Yes — Vinod Patil and Yogesh Patil	No — generic 'expert trainers' branding
Personal GCP sandbox per student	Yes — provisioned in week 1, used through capstone	Shared institute account or screen-share only
BigQuery depth	Partitions, clustering, optimisation, BigLake federated queries — full week	Slides only or basic SELECT
GKE coverage	GKE Autopilot AND Standard mode hands-on	Theory only
Vertex AI / Gemini	Full week — Workbench, Pipelines, Vector Search, Gemini 2.5 Pro	Not covered or marketing-only mention
IaC	Terraform 1.7+ with google provider, full week	Console click-through only
Public GitHub portfolio output	Yes — Terraform repos and BigQuery + Vertex AI projects	Rare
Salary data shown	Cited from Indeed Pune + AmbitionBox + Glassdoor + 6figr with source URLs	Single number with no source
Placement support duration after course	6 months, with free re-entry to interview prep	1–3 months or vaguely 'until placed'
Batch size cap	15 students	25–40 students

Compare with whoever you are considering. The right test is whether you can see actual student Terraform repos before you pay.

Google Cloud vs AWS / Azure — Which to Pick in Pune?

GCP vs AWS vs Azure depends on which Pune companies you want to work for. AWS dominates Pune product engineering broadly. Azure dominates Pune captives and BFSI. GCP dominates Pune analytics specifically — Tiger Analytics, Fractal, ZS, MathCo, plus the data-engineering arms of Persistent and BMW TechWorks autonomous-driving teams.

Choose GCP if your goal is Pune analytics-engineering roles, data-platform startups, or you specifically want BigQuery + Vertex AI depth. Choose AWS if your goal is product engineering / startups / breadth. Choose Azure if your goal is captives / .NET / Microsoft ecosystem.

Honest recommendation: GCP is a smaller market in Pune than AWS / Azure but pays well per role and has thinner competition. Pick GCP if you have a specific analytics target. Most senior cloud engineers eventually know all three at a working level.

Prerequisites & How to Start

Prerequisites: basic Linux command line, basic Python or Bash scripting, comfort with at least one programming language at a junior level. You do NOT need prior cloud experience — we start from creating a GCP account in week 1.

Decide your mode — classroom in Kothrud, online live, or weekend
Check the upcoming batch dates
Book a free 30-minute counselling call
Confirm enrolment and complete pre-course orientation (gcloud install, GCP free trial)
Show up to day one with a laptop running 64-bit OS and a credit card for GCP free-trial signup

Frequently Asked Questions

How long does GCP training in Pune take at Archer Infotech?+

Approximately 2.5 months — 10 weeks of structured curriculum plus 2 weeks of capstone and interview preparation. The weekend batch stretches over ~4 months at the same content depth.

What is the salary of a GCP Cloud Engineer in Pune?+

Indeed Pune reports an average of ₹7.80 lakh per year for GCP Cloud Engineer (May 2026). Mid-level GCP Solutions Architects (3–6 years) earn ₹14–22 lakh per Glassdoor. Senior GCP Architects / Data Engineers earn ₹26–45 lakh nationally with Pune trending within ±10%.

GCP or AWS or Azure?+

GCP for Pune analytics (Tiger / Fractal / ZS / MathCo). AWS for Pune product engineering / SaaS / fintech (Persistent, BMC, startups). Azure for Pune captives / .NET / Microsoft ecosystem. The right answer depends on which Pune companies you want to work for.

Will I work on real projects?+

Yes — three capstone projects: (1) three-tier architecture with Terraform, (2) data analytics pipeline with BigQuery + Cloud Composer, (3) Vertex AI RAG service with Gemini.

Is BigQuery covered in depth?+

Yes — weeks 5–6 include a full module on BigQuery (partitions, clustering, query optimisation, BigLake federated queries, BigQuery ML). BigQuery is the GCP differentiator and the reason most Pune analytics teams are on GCP.

Is Vertex AI / Gemini covered?+

Yes — week 9 is dedicated to Vertex AI Workbench, Pipelines, Model Garden, Vector Search, Agent Builder, and Gemini 2.5 Pro on Vertex AI. Capstone Project #3 is a Vertex AI RAG service.

Are weekend GCP classes available in Pune?+

Yes — Saturday and Sunday, 09:00–13:00, stretched over ~4 months instead of 2.5.

What is the fee for the GCP course in Pune?+

Course fees range from ₹20,000 to ₹90,000 depending on mode and concession. GCP free-trial credits ($300) cover lab work for most students.

How is this different from your GCP Associate Cloud Engineer course?+

This GCP Training programme is the foundation cloud-engineer course — 2.5 months of hands-on GCP engineering with broad service coverage. The GCP Associate Cloud Engineer course is a separate exam-focused track for candidates who already have GCP experience and want concentrated certification prep.

What support do I get after course completion?+

Six months of active placement support, referrals via our alumni network, mock interviews, and salary negotiation coaching.