Tech Talent Acquisition

When I was recruiting at a startup, I didn’t have LinkedIn Recruiter or fancy sourcing tools. So I got creative and turned to Google. There’s a trick called X-ray search that recruiters use to find talent. But job seekers can flip it to find roles that aren’t showing up on LinkedIn or job boards. It works because you’re searching company job boards directly specifically sites hosted by Greenhouse, Lever and Ashby which are the three most common platforms used by startups, tech companies and design forward teams to post jobs. Here’s how it works: Say you’re a Product Designer looking for remote roles. Pop this into Google: site:jobs.lever.co OR site:jobs.greenhouse.io OR site:ashbyhq.com "product designer" AND "remote" You’ll get real-time openings, straight from company career pages. Looking for something location-based and you’re a Social Media Manager in LA, use this: site:jobs.greenhouse.io OR site:jobs.lever.co "social media manager" AND "Los Angeles" You can plug in any title, industry or location that matters to you like “brand designer,” “UX internship,” or “marketing. coordinator” This is how I found amazing candidates when I had zero tools. Now I’m sharing it with you because the best jobs aren’t always on the front page. Try it and let me know what you find!

Demand for software engineers is as bad now as it was during the peak of the pandemic. In 18 months, the number of data engineering job openings on LinkedIn has been cut in half. It’s not the end of technical roles, but the data shows demand is changing. Trying to replace engineers with low-code tools and #AI code generators fails. However, most platforms now support technical and nontechnical co-development environments. A new type of technical role has gained traction in businesses. Smaller software and data teams build frameworks and tools for nontechnical developers on a co-development platform that’s available to anyone in the business. Meta and JPMC implemented enterprise-wide co-development platforms and are seeing massive benefits. One or two technical resources are embedded into the nontechnical team to support their development efforts. Solutions are developed faster and more closely meet customer and business needs because domain experts build them. A few advanced R&D teams still operate in the business but focus on building larger, more innovative products. Teams supporting incremental features and internal operations initiatives are going away. While demand is falling in some areas, it’s rising for the embedded, business-facing technical roles. There are three levels: 1️⃣ Domain Expert Technical ICs: Value-centric #data engineers, data analysts, and software engineers who support nontechnical developers and are embedded into their organizations. 2️⃣ Product Manager Engineers: Technical capabilities with deep product strategy expertise. They know what to build and can implement high-value features independently or with a team. 3️⃣ Technical Strategists: Technical experts who work with executive and C-level leaders. They bring data, models, and rapid product development capabilities to the strategy planning and implementation processes. I have taught data and AI strategy, #ProductManagement, and value-centric capabilities to technical ICs for 8 years to meet today's demand shift. Technical roles are evolving, and amazing opportunities exist for people who adapt.

Hello to those hiring generative AI engineers and generative AI architects. Your job descriptions are all over the place, you need to define them properly. The roles of a generative AI engineer and a generative AI architect, while interconnected, focus on different aspects of working with generative AI systems. Here's a breakdown of their primary differences: ### Generative AI Engineer **Role and Responsibilities:** 1. **Development and Implementation:** - Writing and testing code for generative models (like GPT, GANs, etc.). - Implementing algorithms and neural network architectures. - Fine-tuning pre-trained generative models to achieve desired outputs. - Programming in languages such as Python, and using frameworks like TensorFlow, PyTorch, etc. 2. **Experimentation:** - Conducting experiments to improve model performance. - Monitoring the training process and making necessary adjustments. 3. **Data Handling:** - Preparing and processing data for model training. - Handling datasets, including data cleaning and augmentation. 4. **Quality Assurance:** - Testing models to ensure they generate high-quality, relevant content. - Debugging and troubleshooting model issues. ### Generative AI Architect **Role and Responsibilities:** 1. **System Design:** - Designing the overall architecture of generative AI systems, ensuring they meet business and technical requirements. - Deciding on the integration of various components (data pipelines, model deployment frameworks, user interface). 2. **Strategic Planning:** - Defining the roadmap and selecting the appropriate technologies and tools for generative AI projects. - Making high-level decisions about model selection, system requirements, and scalability. 3. **Coordination and Leadership:** - Coordinating with stakeholders (product managers, data scientists, engineers) to ensure the design aligns with user needs and business goals. - Leading and mentoring engineering teams. 4. **Optimization and Performance:** - Ensuring that the system architecture supports efficient model training and inference. - Implementing measures for scalability, maintainability, and security. 5. **Innovation:** - Keeping abreast of the latest advancements in generative AI and integrating new techniques and methodologies where appropriate. - Proposing new ideas and solutions that leverage generative AI for business value. ### Summary - **Generative AI Engineers** are more hands-on with the coding, model training, and testing aspects. They focus on the practical development, fine-tuning, and implementation of generative models. - **Generative AI Architects** are involved in the high-level design, strategic planning, and system integration aspects. They focus on the broader system architecture, ensuring that the generative AI system fits within the organizational infrastructure and meets overall goals.

Streamline Your New Hire Journey with SAP SuccessFactors Onboarding SAP SuccessFactors Onboarding is more than just an orientation tool—it's a pivotal solution that connects seamlessly with other modules to ensure new hires feel supported from day one. Here's how it integrates with key modules: 👉 Recruiting: Automatically transition candidates into the onboarding process directly from their application. 👉 Employee Central: Facilitate smooth conversion of candidates into employees, whether or not they're sourced via Recruiting. 👉 Learning: Assign courses to new hires even before their first day, ensuring they hit the ground running. 👉 Performance & Goals: Empower employees by setting goals as part of their onboarding journey with templates for New Hire Goal Management. 👉 DocuSign: Enable digital signatures for forms, ensuring compliance and ease across devices. 👉 Qualtrics Employee Lifecycle: Collect actionable feedback through automated surveys triggered upon program completion. Opportunities for Enhanced Integrations: There are potential areas to amplify the onboarding experience: 👉 ITSM tools like ServiceNow: Automate provisioning of equipment and systems access for new employees. What integrations do you think are essential for a next-gen onboarding process? Share your thoughts below! 👇 #SAPSuccessFactors #Onboarding #HRTech #EmployeeExperience #Integration

A job description is not a checklist of requirements. It is a hiring manager telling you exactly how to get the job, and most engineers just do not know how to read it. I have coached engineers who self-rejected from roles they were perfect for and others who landed roles they did not qualify for on paper. The difference was never the resume.  It was how they read the posting. Here is how to read a job description like the person deciding who gets hired: 𝗦𝘁𝗲𝗽 𝟭. 𝗙𝗶𝗻𝗱 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗯𝗲𝗵𝗶𝗻𝗱 𝘁𝗵𝗲 𝗿𝗼𝗹𝗲 Weak: Reading the description as a list of tasks you need to have already done. Strong: Asking what problem this team is hiring to solve and where this role fits in it. Companies do not hire for tasks. They hire to solve a problem.  Find it and you know exactly what to emphasize. 𝗦𝘁𝗲𝗽 𝟮. 𝗦𝗲𝗽𝗮𝗿𝗮𝘁𝗲 𝘁𝗵𝗲 𝗺𝘂𝘀𝘁-𝗵𝗮𝘃𝗲𝘀 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝘄𝗶𝘀𝗵 𝗹𝗶𝘀𝘁 Weak: Self-rejecting because you do not match every bullet point. Strong: Identifying the three or four requirements the role actually depends on and focusing there. Most job descriptions are a wish list.  Matching most of a senior role is usually enough to be a serious candidate. 𝗦𝘁𝗲𝗽 𝟯. 𝗠𝗶𝗿𝗿𝗼𝗿 𝘁𝗵𝗲𝗶𝗿 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗯𝗮𝗰𝗸 𝘁𝗼 𝘁𝗵𝗲𝗺 Weak: Describing your experience in your own words and hoping they connect the dots. Strong: Using the exact framing from the posting to describe the work you have already done. The reader spends seconds deciding if you fit.  Speak their language and you make that decision easy. 𝗦𝘁𝗲𝗽 𝟰. 𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗹𝗲𝘃𝗲𝗹 𝘀𝗶𝗴𝗻𝗮𝗹𝘀, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝘁𝗵𝗲 𝘁𝗶𝘁𝗹𝗲 Weak: Trusting the title to tell you the seniority of the role. Strong: Reading the scope, ownership, and impact described to understand the real level. The same title means different things everywhere.  The responsibilities tell you the truth the title hides. A job description is not a wall you have to clear. It is a map, if you know how to read it. The engineers who target well apply to fewer roles and hear back from more of them. Save this post before your next application. If you keep applying and not hearing back, comment TARGET.  Let me show you how to read the roles you actually want.

AI isn’t just creating new tools, it’s creating entirely new careers. And as I move deeper into my own AI journey, I want to take you with me. If you're building a team, hiring talent, or planning your own career path, understanding this new landscape is no longer optional. It’s a competitive advantage. Here's a breakdown of emerging AI roles that are essential for building the future of AI-driven systems: AI Roles: 1. Model Manager Oversees development, deployment, and performance of ML models. Tech Stack: Python, TensorFlow, Kubernetes, Docker. 2. ML Engineer Designs, develops, and deploys scalable machine learning solutions. Tech Stack: Python, PyTorch, AWS/GCP, SQL. 3. Data Engineer Creates and maintains data pipelines for model training. Tech Stack: Python, Spark, Kafka, AWS/GCP. 4. AI Architect Designs scalable AI systems integrated with existing infrastructure. Tech Stack: Python, Kubernetes, Microservices, Docker. 5. Data Scientist Analyzes data to build predictive models and generate insights. Tech Stack: Python, R, TensorFlow, Hadoop. 6. AI Developer Develops AI applications, integrating ML algorithms into production. Tech Stack: Python, Java, TensorFlow, Kubernetes. 7. Decision Engineer Builds systems to automate decision-making using AI models. Tech Stack: Python, ML frameworks, Cloud platforms. --- Emerging AI Roles: 8. Analytics Engineer Transforms data into actionable insights using analytics tools. Tech Stack: Python, SQL, Tableau, Apache Airflow. 9. AI Product Manager Manages the lifecycle of AI-driven products, bridging technical teams and stakeholders. Tech Stack: Jira, Python (basic), Agile methodologies. 10. UX Designer (AI) Designs user interfaces for AI applications, ensuring seamless AI-powered experiences. Tech Stack: Figma, Adobe XD, HTML/CSS, JavaScript. 11. Head of AI Leads AI strategy across the organization, ensuring alignment with business goals. Tech Stack: Leadership tools, Cloud platforms, Project management software. 12. D&A and AI Translator Translates business needs into technical AI solutions, bridging the gap between teams. Tech Stack: Python, SQL, Jira, Agile. --- Must-have AI Roles: 13. AI Risk and Governance Specialist Ensures compliance with legal, ethical, and regulatory standards for AI systems. Tech Stack: Compliance tools, Risk management software. 14. Model Validator Validates the accuracy and reliability of ML models in real-world environments. Tech Stack: Python, Scikit-learn, TensorFlow. 15. Prompt Engineer Optimizes large language models by fine-tuning prompts for better performance. Tech Stack: Python, NLP frameworks, Hugging Face. 16. AI Ethicist Ensures AI systems are fair, transparent, and ethically sound. Tech Stack: Ethical guidelines, Compliance tools. If you want to stay ahead of the AI curve, follow along. Let’s navigate the AI era together. Which of these roles fascinates you the most, or aligns with your next career move? Comment below. #AI

When I worked in big tech recruiting, part of my job was attending kickoff calls with hiring managers - and the same pattern showed up again and again: These were the initial meetings where we’d talk through the role: the job description, what they were really looking for, and the gaps on the team. More often than not, before we ever talked about sourcing or applications, the hiring manager already had a short list of names in mind. Not because the role was rigged. But because they’d worked with, heard of, or seen those people before. That’s when it really clicked for me why visibility matters so much. You can do great work. You can be highly capable. But if you’re not known, you’re rarely the person who gets tapped when opportunities open up. The takeaway: Don’t just focus on doing great work. Focus on making sure the right people know you - and know how you think - before you need anything. This is why I’m so bullish on LinkedIn. It lets you build familiarity long before opportunity shows up.

Recruitment continues to evolve and what impressed me recently is how AI-Assisted Search combined with Semantic Search is transforming the way recruiters identify, match, and source talent.   Previously, sourcing depended heavily on strict keyword matches. That meant many qualified candidates were easily overlooked simply because they described their skills differently. Instead of matching text, it understands meaning, patterns, and hiring intent.   With semantic and AI-powered search capabilities, platforms like "LinkedIn Recruiter" can now: - Interpret role intent — not only job titles. - Recommend candidates based on capability similarity. - Expand talent pools beyond narrow direct keyword usage. - Reduce manual filtering time and speed up shortlisting.   This evolution shifts sourcing from manual filtration to strategic matching. Recruiters can now spend more time on value-added activities, candidate engagement, quality assessment, and influencing hiring decisions.   Stronger insights, smarter matching, and faster precision sourcing will be one of the biggest competitive advantages in today’s talent market. #TalentAcquisition #LinkedInRecruiter #SemanticSearch #HiringExcellence #FutureOfRecruitment #SaudiArabia

There is an old saying: “I will take the familiar hell over the unfamiliar heaven.” That philosophy is human nature. But I understand how it can prevent organizations from making the right leadership choice. Because when the external world feels uncertain, boards often anchor on what feels safe - and in hiring, that often means turning inward. The result is a bias toward internal or known candidates, even when the data does not support that the “known quantity” is the better choice. This dynamic is not new. The Ellsberg Paradox, a well-known decision theory experiment, illustrates that people almost always prefer the jar with known probabilities over the one with unknown probabilities…even when the unknown jar could hold a better outcome. But the perception of decreased risk in hiring someone you already know - someone from inside the company, or even someone who shares your background - is enormous and often not rooted in evidence. To start to counter this bias, boards and decision-makers can do three things: 1. Be centered and concrete in what you are truly selecting for. The best boards center on the problem to solve, not the comfort of who might solve it. 2. Really get to know people. Spend the time to understand external or unfamiliar candidates and internal or “familiar” candidates. Reduce the asymmetry between the known and the unknown, but also don’t assume things about the people you know. 3. Add context. Evaluate what could be, not just what is. Seeing potential requires imagining the organization with a new leader in place, not simply repeating the familiar. We are entering a moment of swift technological growth and market volatility - a moment where ambiguity and structural change coexist. These conditions tend to polarize boards between playing it safe and swinging for transformation. The best boards recognize the bias toward the familiar, name it out loud, and design processes to overcome it. Sometimes, the unfamiliar heaven is exactly where the next great leader is.

Cloud Infrastructure Engineers, System Administrators, and Site Reliability Engineers (SREs) all deal with IT systems and infrastructure, they focus on different aspects of operations, scalability, and reliability. 🔧 1. System Administrator (SysAdmin) Focus: Maintaining and managing on-premise or cloud-based servers, systems, and networks. Key Responsibilities: Install, configure, and maintain servers and OS. Monitor system performance and troubleshoot issues. Manage backups, patches, and user permissions. Usually reactive and operational (responding to issues as they arise). Tools: Linux/Windows servers, Active Directory, Bash/Powershell, Nagios, Puppet/Chef (sometimes). ☁️ 2. Cloud Infrastructure Engineer Focus: Building and maintaining scalable cloud environments (AWS, Azure, GCP). Key Responsibilities: Design and deploy cloud-based architectures. Manage cloud services like EC2, VPCs, Load Balancers, Kubernetes. Handle networking, security, storage, and compute resources in the cloud. Often involved in DevOps automation and IaC (Infrastructure as Code). Tools: Terraform, CloudFormation, Kubernetes, Docker, AWS CLI, Azure DevOps, GCP Console. ⚙️ 3. Site Reliability Engineer (SRE) Focus: Ensuring systems are reliable, scalable, and automated—bridging software development and operations. Key Responsibilities: Write code to automate infrastructure and operations. Define SLAs, SLOs, and SLIs for system reliability. Monitor availability and performance proactively. Perform incident response and root cause analysis. Strong DevOps mindset: reliability as a software engineering problem. Tools: Prometheus, Grafana, PagerDuty, Ansible, Go/Python, Kubernetes, CI/CD pipelines.