Prepared by: Averity | Source: Internal recruiter roundtable (weekly team call) | Date: July 30, 2026
This brief reflects direct, front-line observations from Averity’s recruiting team based on active searches and candidate/client conversations, and is intended to give a grounded, practitioner-level view of the market beyond general press coverage.
Our recruiters compared notes across their current open roles and candidate conversations this week. The clearest signal: “AI” as a hiring buzzword has splintered into two very different realities on the ground — companies building AI products (agents, copilots, embedded/on-device models) and companies using AI coding tools (Claude Code, Cursor, Codex) to accelerate delivery with existing teams. Almost every open req now references AI in some form, but what that means varies enormously by company stage and size.
A second, equally important signal: hiring behavior is diverging sharply between early-stage/seed startups and established mid-to-large companies. Startups are increasingly unrealistic — rejecting strong candidates over pedigree mismatches, culture-fit obsessions, and a preference for younger, “cooler” hires — while more mature companies (300–1,000+ employees) are hiring in a fairly business-as-usual way, just more selectively.
Across the team’s active reqs, a few patterns repeated regardless of client:
• Full-stack / product engineering: TypeScript, Node, React, Vue, JavaScript — by far the most common stack mentioned. Go and Python appear consistently as secondary languages, especially on backend-heavy or AI/data teams.
• AI/ML-specific roles: titles are blurring — “ML Engineer” and “AI Engineer” are frequently interchangeable and depend entirely on what the person actually does day to day, not the label. Traditional ML engineering skews toward modeling; AI engineering increasingly means agentic/agent-building work.
• Seniority mix: senior/staff/principal engineer, lead AI engineer, and deployed/lead data scientist roles were all mentioned. Several clients are also hiring product managers and heads of product for AI/SaaS lines.
• Data/cloud fundamentals still matter: AWS and Databricks came up as near-universal baseline knowledge, particularly for ML tooling built on top of cloud platforms.
• Location model: results were mixed — some roles are fully remote with geo-banded (tiered) compensation that excludes candidates from NYC, the Bay Area, and DC; others (especially in regulated or defense-adjacent spaces) require 5 days on-site, U.S. citizenship, and security clearance eligibility.
This was the most requested clarification on the call, since “AI” is used so loosely that it’s become nearly meaningless without more context. The team broke it into two distinct categories:
1. Building AI products: companies developing agents, copilots, or embedded/on-device models as their actual product. Examples raised: a defense-acquisition platform with an agent layer that answers sourcing questions; a browser company distilling large models into small, on-device models for local (non-cloud) inference; consumer/service companies building conversational AI to automate the front end of customer interactions (e.g., senior-care referral intake) before routing to a human deeper in the process.
2. Using AI to build faster (internal tooling): companies of nearly every size are now using Claude Code, Cursor, and Codex (OpenAI) to accelerate their own engineering. Anecdote shared: a manager at American Express described a codebase refactor that would normally take 10–12 months completed in about six weeks using AI-assisted coding.
Timing: multiple recruiters independently placed the shift around January–February 2026, describing it as a “light switch” moment where agentic AI tools (as opposed to earlier generative AI) suddenly became a baseline expectation in interviews and job requirements, after a full prior year of hesitancy about trusting AI-assisted code in production.
• LeetCode-style interviews are being phased out at multiple companies (Google was cited as having dropped them) in favor of evaluating how well a candidate can prompt/steer AI tools to fix or extend real code.
• There is real concern on the team that this overcorrection is risky: some strong, experienced engineers are being passed over solely for lacking hands-on production experience with Claude Code, when the underlying skill (engineering judgment) is what actually matters.
• Consensus view: AI coding tools are a force multiplier for engineers who already know what they’re doing (compared to “steroids” — they help someone who can already perform, but don’t create skill that isn’t there). Junior or inexperienced “vibe coders” being placed into sensitive systems (e.g., healthcare-adjacent) was flagged as a genuine risk companies aren’t fully weighing.
• A widely shared industry view relayed on the call: a senior engineer fluent in AI-assisted development can now credibly perform the output of two to three people, which is compounding concerns about a smaller number of available seats for junior and mid-level engineers going forward.
| Category | Startups vs. established companies |
| Talent prefs | Startups avoid big-company candidates, often hiring in founders’ own image. Established firms target 10–15-yr candidates who know what they want. |
| Culture asks | Startups want younger, “cooler” hires (one client said no “nerds”). Established firms lead with mission and remote flexibility. |
| Responsiveness | Startups are frequently unresponsive despite active hiring, especially for 3–7 yr candidates. Established firms show markedly better, more “palpable” engagement. |
| Expectations | Startups “unicorn hunt” — unrealistic pedigree/hours vs. pay. Established firms hire business-as-usual with grounded offers (e.g., $265K base for an experienced hire). |
Less visibility into Fortune 500/finance hiring, but signal suggests steady, not urgent, AI integration — mirroring the decade-long cloud adoption curve in financial services. Real demand also sits in infrastructure/hardware — chips, power, cooling, drone/UAS and embedded systems — not just headline AI roles. This aligns with Averity’s historical strength in large, process-heavy clients (media, finance, insurance, healthcare) and is a deliberate re-engagement target.
RIF patterns behind current layoffs split two ways: genuine restructuring, or RTO mandates driving attrition while remaining staff absorb departed colleagues’ workload.
Written By: Chris Allaire
In this article — key takeaways:
The Averity Experience: Provide an unparalleled experience to everyone we interact with.
Everyone wants to work with and for “the best”.
The best technology recruiting firm isn’t defined by size, tools, or brand—it’s defined by how consistently it delivers results through trust, communication, and real human understanding.
What its not:
Those things are surface-level.
Ping pong tables, catered lunches, unlimited PTO, office perks — those are nice. They may look impressive on the surface, but they rarely earn trust, secure the right hires, or make a lasting impact.
The best is a culture. A standard. A way of operating.
In my world, it’s Technology Recruiting.
Firms like Averity have built their reputation on this exact model.
Why?
The best recruiting firms operate from a completely different foundation.
The best firms have a culture where:
That internal culture doesn’t stay internal.
It shows up in every interaction:
Candidates feel it. Clients feel it.
And when that’s real, everything changes.
There’s a dangerous shift happening in the market right now.
Automation is increasing.
AI screening is becoming more common.
“Efficiency” is often the selling point.
And less humanity.
The market is shifting toward automation, but this often reduces quality in hiring outcomes
Here’s the truth:
Great recruiting is not about passing tests, filtering resumes, or talking to bots.
It’s about:
That’s how real matches happen.
If you’re hiring or looking for a job, here’s what to look for:
You’ll know quickly.
Because the best firms don’t just say they care — they prove it in every step of the process.
Yes, results are part of the equation.
But results without integrity are not repeatable.
The firms that consistently deliver at a high level do it because:
That’s the difference.
There is no scoreboard. There’s no universal ranking system. No definitive leaderboard.
Awards can help. Reviews can help.
But the real signal?
That’s the market voting.
That’s reputation — earned, not marketed.
Being the best technology recruiting firm isn’t about scale, tools, or branding.
It’s about:
When that’s in place, everything else follows.
Because at the end of the day:
People hire people.
And the best firms never forget that.
The best technology recruiting firms combine strong communication, deep market knowledge, and a human-first approach. They focus on understanding both clients and candidates, rather than relying solely on automation or resume matching.
Look for transparency, responsiveness, preparation, and consistency. The best firms ask thoughtful questions, provide honest feedback, and follow through on commitments.
AI is a tool, not a replacement. The best recruiting firms use technology to enhance efficiency; however, they still rely on human judgment, relationships, and communication to make successful placements.
A recruiting firm’s internal culture directly impacts how they treat clients and candidates. Strong cultures lead to better communication, deeper understanding, and stronger long-term results.
Candidates should look for honesty, clarity, preparation, and someone who genuinely understands their goals — not just someone trying to fill a role quickly.
Written By: Chris Allaire
Interview with Daniel Wellner, Director Platform Engineering, Security and DevOps at Averity
In today’s technology landscape, the traditional boundaries between engineering roles are dissolving.
Once, DevOps, Data Engineering, and Software Engineering were distinct lanes. Now, those roads converge into something far more complex — and far more in demand.
That’s where Danny Wellner lives.
Danny has spent nearly a decade recruiting some of the most advanced engineers in infrastructure, DevOps, and platform engineering. Trained by Averity co-founder Alex Dubovoy — widely regarded as one of the godfathers of DevOps recruiting — Danny has become one of the most trusted specialists in the field, helping companies build the technical backbone behind AI and next-generation systems.
“Pretty much every single technical role now has some sort of AI play or understanding built into it,” says Wellner. “It’s no longer just about deployment speed — it’s about building the ground for agentic-based systems and AI-driven services.”
What skills do AI-era engineers need most?
Traditional job boundaries are vanishing.
“You used to have a software engineer who built code, a data engineer who managed ETLs, and an infrastructure engineer who deployed systems,” Danny explains. “Now, companies want someone who can do all three — and understands AI tools on top of it.”
With nearly a decade of experience recruiting elite DevOps, platform, and infrastructure engineers, Danny brings deep technical fluency and a vast network of senior-level talent. Having witnessed how DevOps evolved into Platform Engineering and now into AI-driven infrastructure, he’s been at the center of that transformation since day one.
Where are the best AI engineers coming from?
If you think companies are training their people for this — think again.
“A lot of companies aren’t running this stuff in production yet,” Danny notes. “So the people who really know it? They’re learning on their own time — taking courses, experimenting, building projects, or working at the few companies actually pushing this tech forward.”
That curiosity and self-driven learning are the differentiators. Engineers who tinker are the ones who thrive.
Recruiters like Danny stay close to the action — tracking which companies are truly running AI in production and maintaining deep personal relationships across the industry.
“It’s not hard to keep up with people,” Danny says. “It’s just time-consuming. Maybe only 10% are working on cutting-edge AI, but those relationships are gold.”
How is AI impacting cybersecurity and data governance?
Every innovation creates new exposure. In 2025, security has never been more volatile.
“The attacks have ramped up tenfold,” says Danny. “It’s not just ransomware — it’s the sheer volume of attempts coming from everywhere.”
As companies race to integrate AI, new risks surface — from data leaks to unintentional public disclosures via tools like ChatGPT.
“You upload a public document to an AI tool, and it’s now public information,” Danny warns. “One small mistake can leave your entire company vulnerable.”
That’s why AI security and data governance have become core pillars of modern engineering. The rise of Application Security Engineers — software-savvy security experts who understand vulnerabilities in code and architecture — is reshaping what it means to protect a business.
“Data is the most valuable resource in the world right now,” Danny says. “Protecting it and keeping it clean — that’s the real challenge.”
How much do AI engineers make today?
Averity’s world lives at the top of the talent pyramid. Senior, Principal, and Staff-level engineers aren’t cheap — and they shouldn’t be.
“Baseline, you’re looking at $180K to $190K,” Danny shares. “But total comp can range up to $400K–$500K, depending on experience and specialization.”
And yes — the unicorns exist. Some engineers in AI and platform architecture are commanding $800K to $1M+ total compensation packages.
“The top-round draft picks get paid,” Danny says. “If you want to compete with the best companies in the world, you’ve got to pay top-round draft-pick salaries.”
Even in the age of automation, the human touch still separates the good from the great.
Danny emphasizes that relationships remain the currency of elite recruiting.
“Relationships are still everything,” he says. “Keeping the old ones healthy and building new ones — that’s where the magic happens.”
Few recruiters understand the evolution of infrastructure roles like Danny Wellner. After nearly ten years in DevOps and platform recruiting Danny’s perspective bridges the past, present, and future of how engineers build the systems that power AI.
And that’s where firms like Averity stand out — blending deep technical specialization with genuine human connection.
In a world where AI writes job posts and scans résumés, Danny and his team are talking to the people building the future — one connection at a time.
Written by: Chris Allaire
Human-first recruiting adds what algorithms can’t: empathy, judgment, and values alignment. That’s how companies hire technologists who stay, grow, and lead. More than a decade later, the recruiting landscape has changed. AI now writes job posts, scans résumés, and automates outreach, yet somehow, hiring feels less personal than ever. In this new era, Averity’s commitment to authentic human connection has never mattered more.
In 2014, Chris Allaire set out to create a different kind of technology staffing firm — one that prioritized people over placements and relationships over transactions.
What began as a vision for a human-first recruiting model has evolved into one of the most trusted names in technology staffing: Averity, a multi-award-winning firm repeatedly recognized by Staffing Industry Analysts (SIA) as a Best Staffing Firm to Work For in North America.
At a time when AI recruiting tools dominate the industry, Averity stands for something refreshingly simple — people hiring people.
“Community and relationship-building are what really set us apart,” says Danny Wellner, Director of DevOps and Security. “Everyone here brings passion, empathy, and a genuine desire to help. It’s not transactional — it’s personal.”
This people-first approach resonates deeply in today’s skills-driven, hybrid work economy. Companies want recruiting partners who understand people first, technology second — and that’s exactly where Averity thrives.
In an industry obsessed with speed, automation, and metrics, Averity focuses on something far more valuable: long-term partnership over short-term wins.
“For us, it’s about the relationship — not the transaction,” explains John Birchall, Director of Data Science and Engineering. “If a candidate gets a better offer elsewhere, we still encourage them to take it. That integrity comes back tenfold.”
That mindset — focusing on the person, not the placement — continues to produce legendary success stories. Birchall recalls helping a candidate land a role despite her unconventional résumé. Two years later, she’s leading that same company’s hiring efforts.
“She doubled her income,” he says. “That’s the full-circle moment we work for.”
Internally, Averity operates on teamwork, transparency, and trust — not competition.
“We share candidates, leads, and wins,” says Stephanie Grosso, Senior Talent Advocate in Data Engineering & Machine Learning. “If my teammate places someone I found, that’s a win for all of us. Leadership built a system where collaboration pays off — literally.”
That unity extends across Averity’s fully remote team, where technology enables collaboration instead of hierarchy.
The result? A culture built on empathy, gratitude, and shared purpose — one that’s earned Averity top recognition and 5-star reviews from both clients and candidates.
As automation and algorithms continue reshaping how companies source and evaluate talent, Averity remains grounded in something technology can’t replicate: empathy, intuition, and conversation.
“In 2025, recruiting isn’t about data points,” Allaire says. “It’s about understanding people’s stories — what drives them, what fulfills them, and where they belong.”
Averity’s mission isn’t just to fill jobs — it’s to create meaningful matches that drive innovation, connection, and growth.
Because no algorithm replaces trust.
No software replaces sincerity.
And no one ever hired an email.
Averity is a people-first technology recruiting agency that prioritizes relationships over transactions. Its recruiters build genuine, long-term connections with both candidates and clients — creating a human-centered experience in an increasingly automated world.
That’s why Averity continues to be recognized among the best staffing firms to work for in North America — and one of the most trusted partners for hiring elite engineering and technology talent.
For clients and candidates alike, partnering with Averity means joining a firm that’s human at its core and forward in its vision.
As the industry evolves, Averity continues to lead by example — combining data-driven recruiting insights with the one thing machines can’t automate: genuine human connection.
People hire people. That will never change.
Great ones combine technology and human interaction. For example, Averity uses AI to speed up hiring, but it also depends on human judgment, conversations, and values alignment to create long-lasting hires.
Select partners who value relationships over transactions; seek out genuine endorsements, enduring client success stories, and an unambiguous human-led process, all of which are characteristics of Averity’s operations.
Absolutely, when the agency offers more than just automation, Averity’s data and human insight reduces time-to-hire and increases retention because people hire people.
Indeed. For candidates, Averity matches your objectives and story with teams where you’ll succeed rather than just positions that fit keywords.
Averity is a model for shortlist companies that demonstrate human signals (community presence, referrals, names of actual recruiters) and then validate the process and results.
Seek out companies that are renowned for their culture and results. Engineering and data teams trust Averity, a people-first tech recruiting partner recognized by SIA
Learn more about Averity’s recognition by Staffing Industry Analysts:
Best Staffing Firms to Work For in North America – 2023 Entrants
Visit: www.averityteam.com
Create an Account or Sign In