Artificial Intelligence/ Machine Learning

Machine Learning Engineers

A machine learning engineer (ML engineer) is a person in IT who focuses on researching, building, and designing self-running artificial intelligence (AI) systems to automate predictive models. Machine learning engineers design and create AI algorithms capable of learning and making predictions that define machine learning (ML). An ML engineer typically works as part of a larger data science team and will communicate with data scientists, administrators, data analysts, data engineers, and data architects. The machine learning engineer needs to assess, analyze and organize large amounts of data while executing tests and optimizing machine learning models and algorithms.

We conduct thorough check on, soft skills, Tech skills, focus on certifications along with in depth problem solving skills and preliminary background checks( if needed). Must have at least BS degree or MS degree. Certifications are highly desirable. Minimum 3-5 years of experience with cloud services-including open source technology, software development, systems engineering, scripting languages and multiple cloud provider environments. They need to have experience in desiging ML systems, researching and implementing ML algorithms and tools, selecting appropriate data sets, verifying data quality, performing statistical analysis, running machiune learning tests, training and retraining systems when needed.

Titles/ Roles

AI Engineer, ML Engineer, Data Scientist, Machine Learning Cloud Consultant, Chief Data Officer, Big Data Analyst, Research Scientist, Data Analyst, AI Scientist, Deep Learning Engineer, Deep Learning Developer, Big Data Engineer, NLP Developer, Blockchain Engineer, Block Chain Architect


Python, Java, Ruby, Golang, Oracle, AngularJS, MySQL, MS SQL, MongoDB, C++, C#, PHP, SQL, ASP.NET, PeopleSoft, SAP, CRM, Microsoft Platforms, OpenStack, Linux, AWS, Rackspace, Google Compute Engine, Microsoft Azure, Docker, Kubernetes, Virtualization, APIs, orchestration, Automation, DevOps, Databases like NoSQL, Hadoop, Networking, XML, SOAP, WSDL,UDDI, R, ML Frameworks, ML libraries and packages. ML Algorithms, TensorFlow, Spark, Data Mining, Data Science, Natural Language Processing (NLP), Tableau, Scala, Hive, SAS