BairesDev

Name: BairesDev
Address: 2 Embarcadero Center, San Francisco, CA, 94111, US
Telephone: +1 408-915-4135
Price range: Not Applicable

BairesDev is your expert partner that works alongside you to build high-quality software — AI for the speed, senior engineers for the precision.

We are the leading Nearshore Technology Solutions company. We architect and engineer scalable and high-performing software solutions to meet the business challenges of our clients. Using our tech expertise and cross-industry experience, we evolve digital transformation into digital acceleration. Our ultimate goal is to create lasting value throughout the entire digital transformation journey. With 4,000+ professionals in 40+ countries, we provide time zone–aligned services to empower Fortune 500 companies and leading brands. Working for clients like Google, Rolls-Royce, Johnson & Johnson, Pinterest, and ViacomCBS, we’ve been reimagining the tech landscape for over a decade. We are Digital Acceleration Experts. We are a company Powered by Technology and Driven by Talent.

09/15/2026

AI is now saving developers 13 hours a week in coding, nearly double last year, but almost none of it has become free time.

The hours went into harder work: deciding whether what the tools produced is correct, and owning that decision.

That finding comes from our Q3 2026 Dev Barometer, a survey of 705 developers and 41 CTOs. 67% report spending more time reviewing AI-generated code than they did 12 months ago, and time spent learning AI and new tooling has more than doubled.

Writing code got faster while judgment did not get any easier, and judgment is where the work sits now. 72% of developers say AI assists with at least a quarter of their code, against 33% a year ago. Code volume increased while the share of the week spent producing it decreased, which means more output moving through the same set of people qualified to approve it.

78% of the CTOs we surveyed increased investment in review and validation over the same period. The layer that used to be absorbed quietly is now being funded on purpose.

The job did not get lighter. It moved up a level, and that is a different kind of engineer than most job descriptions were written for.

➡️ The full report is out now: https://bit.ly/4rfcNQu

09/10/2026

In Q3 2025, developers told us AI was saving them about 7 hours a week in coding. We asked the same question again this year.

Same question, same methodology, and twelve months in which AI assistance stopped being an experiment for most developers and became part of how the work gets done.

The number changed, but where those hours went turned out to be the bigger finding.

The Q3 2026 Dev Barometer will be published on September 15, built on responses from 705 developers and 41 CTOs.

What do you think the number is now?

09/03/2026

Cut headcount to capture AI savings, and the work those roles were doing doesn’t disappear. It reappears, informally, on your senior engineers and operators.

In a new piece on our blog, our Fellow Krishna Vedula, Chief of Technology, Product and Operations at WellnessLiving, describes what happens when companies treat AI as a headcount strategy before redesigning how work gets done. The layers you thought you removed quietly come back: senior engineers become the review layer, QA the requirements-correction layer, operations the exception-handling layer.

His argument is that none of this means AI failed. It means the tools were applied to tasks before the operating model was redesigned. The more durable move is to organize around outcomes, with two roles doing the heavy lifting: AI Product Builders, who compress the distance between a customer problem and shipped software, and AI Operators, who keep those workflows reliable after deployment.

Both depend on governance being built in from the start, with clear answers to who validates the output and who owns the outcome when something goes wrong. Vedula also makes the case that external engineering teams are most useful when they extend ex*****on capacity without taking over the judgment that makes a business distinct.

His full piece lays out the three-step model for mapping AI adoption to outcomes, from naming the outcome to assigning explicit ownership.

➡️ https://bit.ly/3SnSQK3

08/31/2026

AI is far less forgiving of bad data than traditional BI ever was.

Data debt can sit hidden for years while a company runs dashboards and one-off analytics. Then AI arrives and quickly exposes the duplicates, the inconsistent definitions, the missing context, the fields nobody can trace. Scale beyond pilots and it starts showing up as model underperformance, higher iteration cycles, and rising operational costs.

That difference runs through this CIO article by Bob Violino, featuring Juan Nassiff, Regional CTO at BairesDev. As Juan puts it: "We don't treat data remediation as a one-time cleanup. We treat it as ongoing engineering with guardrails that prevent debt from coming right back."

Those guardrails are specific. Automated checks for freshness, completeness, duplicates, and schema changes catch problems before they reach an AI workflow. Ownership is explicit, definitions are shared, and lineage is documented, so teams aren't working from conflicting versions of truth.

Full article on CIO: https://bit.ly/4gu601v

08/28/2026

When a Java service slows down, the first instinct is to tune the JVM: garbage collector settings, heap sizes, flags. That work has a real ceiling. The JVM is simply where the stress becomes measurable, and the cause usually sits somewhere upstream.

GC pauses, heap pressure, blocked threads, and CPU strain tell you where a service is under duress. They tend not to tell you why. The why usually lives in service boundaries, data flow, and a topology that drifts over time.

Carlos Moreira, a senior software engineer who specializes in Java, lays out a two-layer way to work the problem: triage the JVM symptom first, then trace it to the architectural or process decision that created it.

That is where most Java performance issues actually get fixed: in the design, not the flags.

Next time a Java service slows down, how far upstream do you usually have to trace it before the real cause shows up?

➡️ Full analysis here: https://bit.ly/3U5efbH

08/26/2026

Agentic AI inflated sprint velocity 100x. Then leadership made staffing decisions based on the number.

In a new piece on our blog, our Fellow Greg Boone, CEO of Walk West, points to a new KPI the agile methodology community has introduced: Human-Agent Handoff Time, the stretch between an agent signaling it’s blocked and a person successfully picking the work back up. In documented team environments, it averages 5.5 hours per incident.

His argument is that the bottleneck in agentic development was never code generation. Velocity can jump from 50 to 5,000 in a single sprint while delivered business value stays flat. The real constraint moved upstream, to the human judgment needed to interpret what the agent was attempting and make the calls it can’t.

That rewrites the staffing math. If the delay lives in the moment an agent blocks and no one is available to step in, the edge belongs to teams with senior judgment on hand when it happens, including across compatible time zones. Cutting headcount to capture AI savings tends to widen the exact gap it was meant to close.

Boone’s full piece covers what he thinks engineering leaders should report to the board instead of velocity, and how to redesign roles around the work that now creates the most value.

➡️ https://bit.ly/4zH3qwH

08/25/2026

AI is not a settled subject among the people who build software. It's easy to treat that as resistance to manage. It's more useful to treat it as a signal worth listening to.

When engineers are skeptical about a tool, dismissing the hesitation tends to entrench it. Instead, acknowledge it. Move the conversation from "should we be worried about this" to something an engineer can actually work with: what problem am I solving with it, and how does it raise the value I bring to the people who depend on my work?

That reframe is what gets a team curious instead of defensive. Adoption then happens against clear foundations, with human ownership at the center, rather than under pressure.

David Von Dollen, our Head of AI, makes the case in this clip. ▶️

08/24/2026

BairesDev reviews over 2.5 million applications a year. That scale shapes a clear view of what hiring managers are looking for in 2026.

Natalia Rodriguez, our VP of Talent Acquisition, shared her perspective in InfoWorld's piece by Bob Violino on landing a software development job in an AI-focused world.

She put it plainly: AI skills help, but they don't replace the fundamentals.

The engineers who advance are those who combine strong fundamentals (programming, data structures, system design) with newer capabilities such as machine learning frameworks and emerging AI-specific techniques.

Read the full article: https://bit.ly/3U22Rxh

08/21/2026

The fastest, lowest-risk returns from AI in healthcare are showing up in the back office: billing, coding, and scheduling, well before anything touches clinical care.

The numbers are hard to ignore. Studies have found AI-assisted medical coding can cut errors by 20 to 50% and claim denials by 20 to 30%, and administrative AI often reaches measurable ROI within six months when it's built on the right integration and review controls.

Izcóatl Estañol, our AI/ML Circle Leader, makes the case for treating this work like production infrastructure rather than experimentation. His argument: the model is rarely the hard part. Disciplined integration, human-in-the-loop review, and audit trails that hold up under compliance are what decide whether a program ships or stalls.

His piece lays out where healthcare organizations should start, and how to get there without tripping HIPAA.

For healthcare teams starting with AI, are the clearest early returns coming from the back office, or is the pressure to show value pushing you toward clinical use cases first?

➡️ Full analysis here: https://bit.ly/45Y7jQp

08/20/2026

The senior engineers you’ll depend on in five years won’t be the survivors of today’s ticket queue. Time served no longer adds up to seniority.

In a new piece on our blog, Christine Richardson, CTO of Pentair Pool and a member of our Advisory Board, argues the old junior-to-senior pipeline no longer scales. The gradual on-ramp of small tickets is giving way to a steeper curve, where juniors are dropped into higher-leverage problems earlier and judgment matters more than years logged.

As AI absorbs ex*****on work, the value of a senior engineer moves upstream, toward framing problems and steering systems. But compressing or cutting the junior layer creates an institutional-knowledge gap that surfaces three to five years later, when fewer people can explain why the systems were built the way they were.

Her framing is that your product development model is your talent development model. The teams that give juniors real ownership of small features, tight feedback loops, and AI used to deepen understanding rather than skip it are the ones that will actually produce the seniors they say they want.

Richardson’s full piece covers how she screens for systems thinking early, and what replaces the ticket queue as a training ground.

➡️ https://bit.ly/4g8LW4C

Address

2 Embarcadero Center
San Francisco, CA
94111

Telephone

+1 408-915-4135

Website

https://www.bairesdev.com/

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