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Resource Augmentation

Skilled Engineers, When You Need Them

Access battle-tested Big Data, Cloud, and DevOps engineers on-demand — embedded in your team, working your hours, aligned with your goals.

Request Engineers Discuss Your Requirements

The Right Engineer for Your Stack

Finding and hiring senior Big Data, Cloud, and DevOps engineers is hard. Building these capabilities internally takes time your business doesn't have. RadiCorp's Resource Augmentation model gives you immediate access to experienced practitioners — without the overhead of permanent hiring.

Our engineers don't just fill seats — they bring deep domain expertise, production experience, and a practitioner mindset. They embed within your existing team, follow your processes, and deliver real impact from day one.

  • Big Data Engineers: Hadoop, Spark, Kafka, Hive, Flink, data lake architecture
  • Cloud Architects & Engineers: AWS, Azure, GCP — solution design and implementation
  • DevOps & Platform Engineers: Kubernetes, Terraform, CI/CD, GitHub Actions, SRE
  • Data Scientists & ML Engineers: Python, ML frameworks, predictive modeling, MLOps
  • Data Analysts & BI Developers: Tableau, Power BI, SQL, analytics platforms
  • ETL / Data Pipeline Developers: Informatica, dbt, custom pipeline development
  • Tech Leads & Architects: senior oversight for complex multi-team programs
Available Engineer Profiles
Big Data Engineer AWS Architect Azure Engineer Kubernetes Engineer ML Engineer DevOps Engineer Data Scientist Platform Engineer BI Developer Tech Architect
Typical onboarding time: under 2 weeks from agreement
Key Outcomes
  • Immediate access to senior engineering talent (typical start: within 2 weeks)
  • 40–60% cost savings vs permanent hiring (no benefits, taxes, recruiting overhead)
  • No risk of prolonged hiring cycles blocking critical projects
  • Knowledge transfer built in — we upskill your permanent team as we work

Flexible the Way Your Business Needs

Whether you need an engineer for a two-month sprint or a long-term team member for a multi-year programme, we have a model that fits.

Technologies Our Engineers Work With

Our talent pool covers the full enterprise data and cloud stack — from legacy Hadoop environments to modern cloud-native and GenAI platforms.

Apache Hadoop
Apache Spark
Apache Kafka
Apache Hive
Apache Flink
Amazon AWS
Microsoft Azure
Google Cloud
Kubernetes
Docker
Terraform
GitHub Actions
Python
TensorFlow
PyTorch
MLflow
Snowflake
Databricks
Tableau
Power BI

Engineers Who Actually Deliver

We don't place CVs and hope for the best. Every engineer we deploy has been vetted on real enterprise systems, assessed on your specific technology stack, and selected to deliver impact — not just attendance.

Production-Experienced
Every engineer has worked on real enterprise systems, not just certifications and tutorials. They understand production constraints, operational risk, and what it takes to ship.
Rapid Onboarding
Our engineers get productive fast. Most teams feel the impact within the first two weeks — not two months. We train them on your context before they arrive.
Full Transparency
Clear reporting, consistent communication, and no hidden overhead or bench costs. You always know what you're getting and what you're paying for.
Flexible & Scalable
Scale your team up or down as project needs change, with minimal process overhead. Add a second engineer in a week, or step down after a go-live without penalty.
How We Compare
RadiCorp
In-House Hire
Time to Start
< 2 weeks
3–6 months
Cost vs Perm
40–60% lower
Full burden
Scalability
Immediate
Slow & costly
Risk
Low
High (mis-hire)
Knowledge Transfer
Built-in
Varies

From Request to Embedded Engineer

A streamlined process that gets the right engineer into your team quickly, with full alignment on goals and working style from day one.

01

Requirements Brief

We start with a detailed conversation about the role — technology stack, seniority level, engagement duration, team culture, and specific project context. No generic job-spec forms.

02

Engineer Matching

Our talent team matches the brief against our vetted network of practitioners. We shortlist 2–3 profiles within 48–72 hours — you interview and choose who fits your team best.

03

Pre-Onboarding Prep

Before the engineer's first day, we brief them on your systems, tools, team norms, and initial objectives. They arrive ready to contribute — not asking basic setup questions on day one.

04

Embedded Delivery

The engineer works within your team — your standups, your Jira, your Slack, your code reviews. We stay in the background with weekly check-ins to ensure quality and alignment.

05

Handover & Scale

At engagement end, we run a structured handover including documentation, knowledge transfer sessions, and optional mentoring of your permanent engineers. Or we scale the team up for the next phase.

<2 Wks
Typical Engineer Start Time
50%
Avg. Cost Savings vs Permanent
100+
Engineers Deployed
97%
Client Retention Rate

Extend the Value Further

Resource augmentation works best when combined with upskilling your permanent team and keeping augmented systems running reliably after the engagement ends.

Training & Enablement

Upskill your permanent team while our engineers work alongside them. Structured training on Big Data, Cloud, and DevOps ensures your team retains capability after the engagement.

Big DataCloudDevOpsWorkshops
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Managed Support

Once your augmented team completes a build, our managed support service takes over operations — providing 24x7 monitoring, optimization, and SLA-backed reliability without engineering burden.

24x7 MonitoringSREFinOps
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Big Data Engineering

If you need a dedicated project outcome rather than embedded engineers, our Big Data engineering practice delivers end-to-end data platform builds under a managed delivery model.

SparkKafkaData LakeLakehouse
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Stop Waiting on Hiring. Start Building Now.

Tell us what you need and we'll have matched engineer profiles in your inbox within 48 hours — no lengthy procurement, no bench risk.