A personal growth map
Where you are, where you want to go and what is most useful to practice right now. We set a goal you can measure along the way.
BUILD YOUR NEXT CHAPTER
Developer Mentoring: DevOps, AI and Interview Prep
Move forward with something you can show.
We sharpen your professional goal, identify gaps and build a plan grounded in doing: a hands-on project, a portfolio and practice explaining what you built and why.
Talk about One-on-One Professional MentoringTHE RIGHT FIT
This is a fit if…
- Developers and testers who want to go deeper into AI or DevOps.
- People building a portfolio for a technical role.
- Professionals who want focused guidance on a specific problem or project.
A CLEAR DELIVERABLE
What you get along the way.
A project that builds skill
A scope you can actually finish, with room for planning, development, testing and explaining your decisions. The hands-on work is yours.
Presentation and interview prep
Practice questions, system design thinking and presenting your portfolio. Focused feedback on where to go deeper.
HOW WE MOVE FORWARDWe map the path.
We map the path.
Then we move.
- 01
A conversation about your background and goal
- 02
A practice plan and project
- 03
Working sessions with feedback
- 04
Wrap-up and preparing for the next step
THE FULL PICTURE
Developer mentoring is a way to move your career forward without guessing alone what to learn, in what order and how to prove it. Instead of yet another generic course, the work follows your goal: moving into DevOps, getting into AI engineering or tech interview preparation, with a real project and feedback from someone who has walked the path himself.
What developer mentoring is, and how it differs from a course
A course teaches material. Mentoring helps you decide which of that material matters for you, and makes sure it turns into a skill you can show. A good mentor does not do the work for you: they ask the right questions, point out gaps that are hard to see from the inside and give feedback on real work, not textbook exercises.
The difference shows most when there is too much information. You can spend months on certificates, reading lists and courses that lead to nothing a technical interviewer wants to see. Career mentoring in tech brings the focus back to three questions: what the role you are aiming for really requires, what you already have and what the next thing worth building is.
Good mentoring also changes the kind of questions you ask. Instead of which tool to learn, you get to questions like why you chose this solution and what you would do differently if the system grew several times over. Those are exactly the questions interviews test, and exactly the ones that are hard to practice alone.
Who one-on-one mentoring is for
Mentoring is built for people who are already in tech, or close to it, and want to take their next step deliberately. For example:
It is less suited to people looking for a placement track or someone to build their project for them. The hands-on work stays yours, and that is exactly what makes it worth something in an interview.
- Software and automation developers who want to move into DevOps and do not know where to start: Kubernetes, Terraform, CI/CD or cloud.
- Testers and QA engineers who want to go deeper into writing code, automation and infrastructure.
- Developers who want to learn AI at an engineering level: building RAG systems, working with agents and bringing LLMs into a product, not just using a chat window.
- DevOps engineers who want to expand into AI and understand how to run such systems in production.
- People preparing for tech interviews who want focused practice, including infrastructure and system design questions.
- Professionals who want focused guidance on a specific problem from work, such as planning infrastructure for a new project or bringing AI into an existing system.
Formats: a focused session, ongoing mentoring, a project or interview prep
Not everyone needs a long process. The format follows the question you arrive with, and you can switch between formats along the way.
In tech interview preparation, most of the work is not memorizing answers but practicing thinking out loud. Interviewers want to see how you break down a problem, what you ask before answering and how you handle a question you cannot answer. In a DevOps interview that might be troubleshooting a failed deployment or designing a pipeline; in an AI role, designing a RAG system and explaining how you would know it answers correctly. All of this can be practiced, and a mock interview quickly shows what works and what does not.
- A one-off session: right when you have a defined question. For example, whether to invest in Kubernetes or cloud first, how to build a learning plan for moving into DevOps, or feedback on an existing project. You leave with an answer and an action plan.
- Ongoing mentoring: a series of regular sessions over a period of time, with assignments in between. Suited to a role change or learning a new field, where progress builds gradually and someone holds the direction even when motivation dips.
- Project-based mentoring: you pick one project that demonstrates the skill you need, such as a full CI/CD pipeline deploying a service to Kubernetes with Terraform-managed infrastructure, or a small RAG application with systematic evaluation of answer quality. Sessions cover planning, getting unstuck and code review.
- Interview preparation: mock interviews, practice with technical and system design questions, and work on how you present your experience and projects. Available as a few sessions before a specific interview or as part of longer mentoring.
Moving into DevOps or learning AI as a developer: what it really takes
Moving into DevOps is not a list of tools. What interviewers look for is an understanding of the path code takes from commit to production: how the build runs, where artifacts live, how infrastructure is managed as code, how you deploy to Kubernetes and how you find out something broke. People coming from development or automation already know part of that path, so it makes sense to start there and expand outward.
A good starting point for moving into DevOps is to take a small application you have already written, package it with Docker, build a pipeline that tests and deploys it, and provision the environment with Terraform. Each of those steps opens a whole topic to learn, and by the end you have something you can show.
In AI, learning AI as a developer does not mean becoming a model researcher. The skill in demand is engineering: connecting a language model to an organization's data with RAG, building workflows with agents and tools, measuring whether answers are good and running all of it reliably. Here a DevOps background is a real advantage, because an AI system in production needs the same pipelines, permissions and monitoring as any other system.
The principle is the same on both paths: one deep project that covers the process end to end is worth more than ten small exercises.
How mentoring is structured, step by step
The structure is fixed and the content adapts to your goal, so it is always clear where you are in the process.
Intro call and goal
We talk about your background, what you have done so far and the role you are aiming for. By the end it should be clear whether mentoring is the right step and in which format.
Gap mapping
We compare what the role requires with what you already have and choose what to focus on first. Not every gap deserves the same investment, and some close with a single project.
Plan and project
We set near-term goals and a project scoped so you can finish it. The plan is written down, so every session starts with what was supposed to happen and what actually did.
Working sessions and feedback
You work between sessions. In sessions we go over what you built, get you unstuck, review code and infrastructure and update the plan based on what we learned.
Presentation, interviews and next step
You practice explaining the project and your decisions, go through mock interviews and we sum up the next step, including after mentoring ends.
What you leave with and how progress is measured
The main outcome of good mentoring is not a feeling of confidence but tangible things: a project on GitHub you can send to a recruiter, documentation that explains your decisions and the ability to talk about all of it in an interview without reciting. Alongside that you leave with a learning map you can keep following on your own.
A good portfolio project meets a few simple requirements: it has a README explaining the problem and how to run it, it is built and tested in an automated pipeline, it documents the main decisions and what did not work, and you can discuss it in depth without running out of things to say. A project like that answers some interview questions on its own, before anyone asks them.
Progress is measured against goals set at the start and checked along the way. Was the latest milestone completed? Can you now solve on your own a problem that needed help a month ago? Does your explanation of the project sound clearer with each mock interview? When goals stop moving, that is a signal to change the plan, not to keep going the same way.
How to choose a mentor, and common mistakes
A good mentor is first of all someone who does the work you are aiming for, or did it recently. A few things worth checking:
And mistakes to avoid: collecting certificates instead of building one thing in depth; picking a project so big it never ends; trying to learn DevOps, AI and a new programming language all at once; and walking into an interview without having practiced explaining out loud what you built and why.
- Do they actually work with the technologies you want to learn, or teach them from the sidelines?
- Have they made a career move similar to the one you are planning?
- Is the mentoring based on hands-on work and code feedback, or only on conversations?
- Do they give direct feedback, even when it is not pleasant to hear? Overly polite feedback will not help you in an interview.
- Are they promising you a job? That is a warning sign. No mentor can guarantee you will be hired.
Why mentoring with me
I have made the moves most mentees ask about. I started as an automation developer, led automation development technically in C# and CI/CD, combined a DevOps role with leading automation, moved into a full DevOps Engineer role and today work as an AI Solutions Architect. Every step on that path is a question I have already answered for myself.
As an architect, I design and build end-to-end AI solutions: RAG pipelines, agentic and multi-agent workflows and LLM-based organizational platforms, from concept to production. My DevOps background includes Kubernetes, including AKS and OpenShift, Terraform, Docker, Azure, Azure DevOps, GitHub, Nexus, CI/CD pipelines and microservices. That is exactly the material DevOps and AI interviews ask about.
That experience translates into practical mentoring: I know what it takes to move from automation development into DevOps because I made that move myself, and I know what an AI architect needs to understand about infrastructure because that is my daily work.
I have also taught, as an assistant lecturer in a software engineering course at Ariel University, where I learned to explain complex topics to people starting from very different places. I work independently, so mentoring is done directly with me.
WHO YOU WORK WITH
Who works with you.
Bechor Simhaev · AI solutions architect & DevOps
I take an AI idea and turn it into a system running in your organization: RAG over your knowledge, agents and agentic workflows, and LLM-based platforms. From discovery to production.
The difference: I don’t stop at the demo. Hands-on experience in DevOps, Azure, Kubernetes and on-prem clusters in closed environments with EKS Anywhere and OpenShift means the solution is built from day one to run in your environment, with its security and permissions. And I’m independent, with no commissions and no preferred tool, so the recommendation rests only on what is right for you.
GOOD QUESTIONS
Before we start.
A developer portfolio: how do you present a project that demonstrates engineering ability?Does mentoring include job placement?
Mentoring can include interview prep, portfolio work and career direction. It is not a placement service, and there is no promise of a job offer.
Can we focus on a single project?
Yes. Mentoring can be built around a specific project or problem, with scope and outcome agreed in advance.
What level do I need to start?
We look at your background and goal in an intro call. The plan is based on where you are starting from, not a one-size-fits-all track.
What drives the cost of mentoring?
Cost mainly depends on the format: a single session, a package of sessions or ongoing mentoring. It is also affected by how much review happens between sessions, such as going over code and documentation, and whether dedicated prep for a specific interview is needed. After the intro call you receive a proposal based on the agreed plan.
How long does it take to move into DevOps?
There is no single answer. It depends on your background, the time you can invest and the role you are aiming for. A developer who has already worked with CI/CD is in a very different place from someone starting from scratch, so the plan is set after gap mapping, not before.
Are sessions remote?
Most sessions are held over video with screen sharing, since that is the easiest way to go through code, terminals and pipelines together. In-person meetings are possible by prior arrangement.
How often do we meet?
In ongoing mentoring we meet at a regular pace that leaves enough time for work between sessions. The pace is set together and changes with the stage: more often before an interview, less often during a calmer building phase.
Do I need programming experience to learn AI as a developer?
Building AI systems at an engineering level requires a programming foundation: writing code, working with Git and reading documentation. If that foundation is not there yet, we start with it and bring AI into the very first project.
Does interview prep include mock interviews?
Yes. A mock interview runs like a real one, with technical questions, a system design question and questions about your experience, followed by detailed feedback. With your consent it can be recorded so you can see for yourself where your explanation stalls.
Can mentoring be tailored to the stack of the company I am targeting?
Yes. If you know which tools your target company uses, for example Azure, GitHub Actions or managed Kubernetes, the project and practice are built around them. If not, we choose common tools and focus on principles that transfer easily between them.
What if I get stuck on the project between sessions?
That is a natural part of the process. A channel for short questions between sessions can be arranged as agreed, and sometimes the blocker itself becomes the topic of the next session. The most meaningful learning usually happens at exactly those points.
Can I get feedback on my resume and LinkedIn profile?
Yes, as part of preparing for a job search. The feedback checks whether what is written reflects what you actually did and built, and whether the project from mentoring is presented so a technical interviewer understands it quickly.
What happens after mentoring ends?
At the end you receive a summary of what was achieved and a plan for continuing on your own. You can come back for a follow-up session before an interview, after starting a new role or when a new professional question comes up.
Is mentoring useful if I already work in DevOps?
Definitely. Mentoring can help you go deeper in a specific area such as Kubernetes or Terraform, prepare for a more senior role or expand into AI, for example running LLM-based systems in production and building pipelines around them.
LET’S MAKE IT REAL
Let’s find the right step for you.
Have an idea, a need or a question that hasn’t settled yet? Let’s work out together what’s right to do.
Let’s talk A direct conversation. A clear direction. A step that fits you.