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HIRE Learning with Machine Learning Reply: "Open conversation, open feedback, and an open-door policy"

HIRE Learning with Machine Learning Reply: "Open conversation, open feedback, and an open-door policy"

16 Dec, 2022 7 min

The HIRE Learning series gets inside the brains of hiring managers who are looking for smart engineers, passionate Grafana users, and observability enthusiasts to join their teams. Find out what questions they ask, what red flags they look for, and what helps their teams succeed. 

It’s one thing to be adept at coding — it’s another to communicate how that code can provide the best solution for a customer.

It’s that key combination of hard and soft skills that stands out for the hiring managers at Machine Learning Reply, which offers business solutions centered on Artificial Intelligence (AI), Machine Learning (ML), and other future technologies. 

“We’re not just looking for a technical geek,” says Ahmed Mosharafa, who leads one of the Machine Learning Reply teams of over 20 engineers mainly based in Munich. “We need people to be fast-thinking. They can ask questions from different directions to see how our customers think. They are able to create something on the fly by taking different opportunities or different chances. And, in the end, they have to be able to present themselves, their technical knowledge, and their solution — and stand up for all of it.”

The team’s sought-after solutions are varied and ever-changing. Along with expertise in AI and ML, Machine Learning Reply offers customers insights into data science, analytical platforms, DevOps operations, robotics, and quantum computing. They are also part of the global Reply network of highly specialized companies that offer consulting, technology, and digital services, focusing on innovation worldwide. But at the heart of many of Machine Learning Reply’s customer solutions is an observability component, which can include monitoring infrastructures and applications, data pipelines, or machine learning models. When screening job candidates, knowledge about the ELK-Grafana-Prometheus stack is highly valued. While observability is not a hard requirement, Machine Learning Reply consultant’s see Grafana often with their customers, and it’s become a standard tool in many projects.

What is non-negotiable at Machine Learning Reply is having an entrepreneurial spirit, which resonates throughout the Reply ecosystem. The person who thrives in Machine Learning Reply is passionate, technologically curious, and motivated. “If there is an initiative, you can see people get super-motivated to dig into it and do something to achieve that goal," says Mosharafa. “Everyone wants to take the company’s growth to the next level.” 

Find out more about the team dynamic for Machine Learning Reply, how their open-door policy works, and how learning is just as important as doing.

Open positions at Machine Learning Reply: 

Management’s open door plan: “I definitely appreciate an open conversation, open feedback, and an open-door policy,” says Mosharafa of his leadership style. “I have frequent one-on-ones with the 20 people reporting directly to me. I don’t like micromanagement. But if you need me, I have an open-door policy. Recently there was an issue within a project that needed me to do a very deep dive into the solution. I’m happy to do it if needed, but I like people to shine on their own. That’s the goal.”

The one thing you need to be able to tell customers: “Customers come with demands, and if you fulfill their requirements, they come to you with more demands,” says Alexander Götz, who works with Mosharafa. “You need to find a balance: Are the customer requests feasible with the available resources? Are they even possible within the given timeframe? We also have customers who change what they want on a daily basis — or even more frequently than that. Here, you definitely need to have the skills to tell the customer: This is not possible.”

Sharing beneficial intelligence: “The team works on different projects, so some people are working together with a customer while others work on individual projects,” explains Mosharafa. “To bring together the team and their ideas, we have team calls every two weeks, and it’s amazing how people talk about different and diverse tool stacks. Someone could say, ‘I’ve been doing observability and monitoring with a Prometheus-Grafana stack.’ And someone else says, ‘I’ve been using that but taking it in a different direction.’” They’re also teaching each other. “If somebody has a project where the customer requested to implement observability, someone on the team will say ‘I can help you. Let’s have a coffee together and talk about it,’ " say Götz. Mosharafa adds: “Then they go on that journey together.”

What they tell everyone in interviews: “Normally we say in interviews that somebody needs to be close to an expert on a topic within two weeks,” says Götz. “It’s very important for us that somebody gets the necessary knowledge, or at least knows where to find a solution.” If there’s a customer requesting a DevOps engineer who can also build a stack that checks data pipelines, someone who is good with CI/CD systems may be given the assignment, but they will need to pick up the basics around observability fast. “They will have to learn how to work with these tools. They can’t just be sitting there saying, ‘Oh, I have no idea how to do that.’ They need to be open to tackling the challenge.”

Ready-made vs. ready to learn

At Machine Learning Reply, you don’t have to be an expert in all the things on Day 1. “If the person lacks some communication skills or some technical aspects, it’s okay,” explains Mosharafa. “It’s part of our duty to bring people up to speed.” Here’s how the team builds each other up. 

Technical support: When Mosharafa met with an engineer for an open position, the candidate presented strong technical skills but lacked experience in cloud native solutions. The engineer immediately offered to take certification classes in AWS or Azure to prepare for the job. Not only was Mosharafa impressed by the proactive thinking. “We said that we can support him by paying for it,” he says. “You don’t have to come ready-made, but you do have to be ready to learn more.”

Team support: As a project manager, “we help people out of their comfort zone, because it’s only then that people learn,” comments Götz. “When I see that someone lacks certain knowledge, I find an opportunity for them to try something new.” Recently a junior team member didn’t want to take on a project because he feared that he had to be fluent in not only the technology but also the customer’s business as well. “I said to him, ‘No worries. We are here for you,’” says Götz, who helped his colleague manage meetings and debriefed with him afterwards on next steps until he felt comfortable leading on his own. “Nearly a year later, he’s so good, the customer only wants to work with him!” summarizes Götz. “It’s all a learning phase.”

About Machine Learning Reply: As part of the Reply network with over 12.000 employees and a global footprint, Machine Learning Reply offers customized end-to-end Data Science solutions, covering the entire project life cycle – from initial strategy consulting, data architecture and infrastructure topics, to processing data with quality assurance using Machine Learning algorithms. Machine Learning Reply has extensive expertise in the field of Data Science in all key industries of German HDAX companies. Machine Learning Reply empowers clients to successfully introduce new data-driven business models and to optimize existing processes and products – with a focus on distributed open source and cloud technologies. With the Machine Learning Incubator, the company offers a program to train the next generation of decision-makers, data scientists and engineers.

Are you a hiring manager looking for observability experts and Grafana enthusiasts? We’d love to highlight your team, your company, and your open observability positions in our HIRE Learning series. Please reach out to us at to learn more.