ML Research & Development team to accelerate enterprise AI research
Expert Hours
R&D Team Size
Partnership Started In
Client Background
Our client is an international enterprise company operating across multiple regions. They run an internal ML R&D function focused on exploring new AI capabilities and turning promising ideas into production-ready directions.
Success Metrics
We transform rapid growth into clear operating models, defined responsibilities, and execution systems.
How It Started
The client needed to speed up research in an ML domain that was becoming strategically important for them. Their core team was already distributed globally, but they lacked enough hands to explore multiple research tracks in parallel and validate ideas fast.
They asked devPulse to build a compact, high-seniority ML team that could integrate into an existing international setup and contribute beyond “implementation tasks.”
Challenge
This was not a typical “AI integration” request. The client specifically needed engineers who could:
Work with ambiguous, research-grade tasks by validating hypotheses and designing experiments, implementing prototypes and benchmarks, and communicating findings clearly across a distributed, multi-timezone team.
The key challenges were:
Depth of expertise: the team had to go beyond API integration and prompt engineering and actively contribute to new research directions.
Talent scarcity: finding 4 strong ML engineers in a highly competitive market.
Distributed collaboration: seamless integration into a team spread across Canada, the USA, Europe, and India, with predictable delivery.
THE Solution
devPulse assembled a focused ML Research & Development team of 4 engineers.
Selected specifically for research-oriented work (experimentation, evaluation, and model-level thinking), not just application-level integration.
We ensured a transparent hiring process:
- Candidates were sourced and screened against the client’s technical bar,
- Interview loops were aligned with the client’s research needs,
- And each hire was approved by the client before onboarding.
To make the distributed setup effective, we aligned working practices early:
- Shared experiment tracking and documentation standards,
- Clear ownership per research track & regular syncs across time zones
- And a lightweight reporting cadence to keep progress visible without slowing the team down.
Let’s enhance your team together
Services provided by devPulse
Talent Acquisition
We source, screen, and hire engineers tailored to your stack and domain, ensuring fast ramp-up and strong technical fit.
Workspace Management
We provide and manage office/remote workspace logistics so teams can start quickly and operate smoothly.
Equipment Management
We procure, provision, and maintain laptops and peripherals, including secure setup, replacements, and lifecycle management.
HR Support
We handle onboarding, payroll coordination, policies, and team well-being processes to keep delivery stable and retention high.
Legal and Financial Support
We support contracts, compliance, invoicing, and cross-border financial processes to keep engagement transparent and low-risk.
IT Infrastructure
We set up and manage secure development and delivery infrastructure (access control, environments, CI/CD, monitoring) aligned with your requirements.
Outcome
The client significantly increased research throughput by adding a dedicated ML R&D squad that could explore multiple directions in parallel, prototype solutions quickly, and contribute to technical decision-making at a research level.
devPulse delivered a high-performing team of 4 ML engineers who integrated smoothly into the client’s global organization.
Helped them accelerate experimentation and discovery—without limiting the work to prompt engineering or API-level integration.
