When setting up a large-scale recruiting process, it’s important to focus on hiring experienced engineers and talent who can boost the company’s overall know-how or fill skill gaps. The competitive hiring environment makes it hard to find qualified candidates, since there may be only a few people who meet the criteria and many companies compete for the same talent pool. Treat the hiring pipeline like a sales funnel and continuously measure its “temperature” to ensure efficient and successful hiring efforts. Without constant monitoring, time gets wasted and inefficiencies arise in the recruitment team. Success in large-scale hiring relies on the team’s ability to fill key roles quickly and spot and remove bottlenecks to optimize workflow. Experimenting with different job descriptions for the same role across various channels can help find which approach brings more quality candidates into the pipeline. This, however, adds extra work for an already busy hiring team. Setting firm deadlines for candidates to complete each phase of the hiring process and tracking hires per week helps keep a steady pace and clearer forecasts for future hiring needs. To support a business plan and hire at scale, you need a well-defined career development system to assess candidates during hiring. This enables more objective evaluation of potential hires and ensures you bring on the right people to meet the company’s needs. Balancing your hiring efforts and keeping a steady number of hires per week lets the team manage the process better and plan future needs. Ultimately, by continuously measuring and improving the speed and efficiency of the hiring pipeline, companies can increase their chances of building a strong, talented team.
The main goal of the “Building a Data-Driven Hiring Machine” project is to establish a data-driven hiring process to recruit experienced engineers and talent, boost the company’s overall know-how, and fill skill gaps to ensure efficient and successful hiring efforts.
There’s a need for a founding engineer experienced in Ethereum and smart contracts for a startup. The company wants someone with solid computer science fundamentals and problem-solving skills, along with knowledge of Ethereum, smart contracts, Solidity, Golang, and other programming languages. But when a junior recruiter posts the role, they struggle to identify the specific requirements and find suitable candidates. This often requires intervention from an HR manager or senior developer to refine the job post and provide details so the junior recruiter can search the company’s resume database effectively. To solve this, the company plans to introduce an AI-based hiring platform using semantic search. Semantic search aims to improve accuracy by understanding the searcher’s intent and the contextual meaning of terms in the searchable data space. Here, the goal is to capture the job post’s intent and match it with relevant resumes from the company’s database. Before matching resumes, the company must capture the contextual meaning of all resumes and store it in a suitable format for queries. To extract the resumes’ contextual meaning, the company plans to use the HuggingFace Transformers library, known for its NLP capabilities. The library generates embedding vectors—multi-dimensional float vectors representing the contextual meaning of sentences, words, or paragraphs. These vectors, which can have hundreds to thousands of dimensions, help match the job post with resumes in the database.
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A real-time, hands-on course on Flink and Hadoop covering MapReduce, HDFS, Spark, Flink, Hive, HBase, MongoDB, Cassandra, and Kafka. Extensive engineering experience including DevOps (CI/CD, automation).
The main task of this AALS Software AG project was to deliver a practical real-time course on Flink and Hadoop that covers various Big Data technologies and DevOps practices, with a strong focus on automation and CI/CD processes.
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The main task of this project was to analyze and optimize data storage, processing, and streaming for various use cases, advise on fitting databases, and boost efficiency using IoT streaming and Azure Data Factory for analytics.
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The main task of the “Custom ChatGPT With Custom Knowledge Base” project at AALS Software AG is to develop an advanced chatbot that derives knowledge from its own document sources, interprets human language with GPT-3, and answers questions about uploaded documents.
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The main task of this project is to leverage natural language processing to develop a chatbot that categorizes published text documents and makes them accessible via dialogue to improve data processing and business intelligence.
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The main task of the “Public Administration School Administration” project was to develop a school management system using Delphi, Microfocus Cobol, and HANA (formerly Sybase ASE).
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The main task of this project was to develop municipal administration software using Microfocus Cobol, Sybase ASE, and SUSE Linux, and to plan a phased migration of solutions from a mainframe to Azure, ensuring interoperability of applications and databases.
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Committed and goal-driven leader with determination and strong analytical skills.
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