Principal Machine Learning Engineer

Job Description

Date: Oct 22, 2020As the tech firm that created the mobile world, and with more than 54,000 patents to our name, we ve made it our business to make a mark. When joining our team at Ericsson you are empowered to learn, lead and perform at your best, shaping the future of technology. This is a place where youre welcomed as your own perfectly unique self, and celebrated for the skills, talent, and perspective you bring to the team. Are you in Come, and be where it begins.Exciting Opportunity:The complexity of emerging 5G networks makes manual management and operations of these networks impossible. AI technologies, including Machine Learning, are increasingly being used to drive intelligent automation and autonomous operation of 5G networks that will drive economic and social transformation. Towards this, we have setup a Global AI Accelerator (GAIA) in the US, Sweden and India, with 300 experts, to fast-track our strategy execution.We use a combination of Machine Learning and other Artificial Intelligence technologies to drive thought leadership to automate and transform Ericsson offerings and operations, including new and emerging business. This includes development of models, frameworks and infrastructure where we not only drive AI based product innovation, but also push the AI technology frontiers. We engage in both academic and industry collaborations and drive the digitalization of Ericsson and the industry by developing state of the art solutions that simplify and automate processes in our products and services and build new value through data driven insights.Ericsson is now looking for Principal Machine Learning Engineers to significantly expand its global team for AI acceleration for our group in Bangalore and Chennai.Role Summary:As a Principal Machine Learning Engineer (MLE), you will be leading efforts for AI model deployment at scale, involving edge interfacing, ML pipeline and design of monitoring and alerting systems for ML models.You are an expert software engineer with experience building large-scale systems and enjoys optimizing systems and evolving them. The Machine Learning Engineer will primarily support our data scientists in parallelizing/distributing the machine learning code to leverage large volumes of data. You are self-directed, comfortable in understanding the ML pipeline, and harden the ML pipeline to handle corner cases that help address real-field scenarios.Key Responsibilities:

• Lead multiple productization/deployment projects within a certain product/business.
• Manage communication, planning and collaboration with business
• Design, and implement horizontally scalable solutions, optimizing insight delivery & exposing them as microservices.
• Convert stand-alone code to work in parallel/distributed environments.

• Design & develop monitoring framework & leverage it to help troubleshoot/optimize deployments
• Contribute to IP creation for Ericsson in AI/ML
• Assist in building and optimizing ML code - converting code to deploy in edge quantization of deep learning models, mobile/low footprint code development, etc.

Key Qualifications:

• Bachelors/Masters/Ph.D. in Computer Science, Information Systems, Data Science, Artificial Intelligence, Machine Learning, Electrical Engineering or related disciplines from any of the reputed institutes.

• Overall industry experience of around 15+ years, at least 8 years experience in developing & productizing large-scale systems especially in the data science space.
• 8+ years of experience in the following:

• Software/tools: Hadoop, Spark, Kafka, etc.
• Relational SQL and NoSQL databases, including Postgres and Cassandra.
• ML & data pipeline and workflow management tools: Azkaban, Luigi, Airflow, Dataiku, etc.

• Stream-processing systems: Storm, Spark-Streaming, etc.
• Object-oriented/object function scripting languages: Python, Java, Scala (Advanced level in one language, at least)

• Experience deploying, debugging and optimizing ML models to handle & scale to big data.
• Experience developing SaaS with deep knowledge in designing for cloud
• Good knowledge in algorithms & data structures, especially for handling large-scale data

• Experience working in ML frameworks such as Tensorflow, PyTorch, etc
• Experience converting code to work in parallel/distributed environments
• Experience converting code to deploy in edge - quantization of deep learning models, mobile/low footprint code development, etc.
• Strong analytic skills related to working with unstructured datasets.
• Good understanding of model validation techniques/metrics

• Design APIs for AI/ML models with focus on business, modularity and versioning
• Working knowledge of message queuing, stream processing, and highly scalable big data stores.
• Experience with Docker containers, orchestration systems (e.g. Kubernetes), continuous integration and job schedulers.
• Knowledge of server-less architectures (e.g. Lambda, Kinesis, Glue).
• Experience with cloud native technologies, microservices design and REST APIs.

Soft Skills:

• Good communication skills in written and spoken English

• Creativity and ability to formulate problems and solve them independently
• Experience in writing and presenting white papers, journal articles and technical blogs on the results

Additional Requirements:

• Applications/Domain-knowledge in Telecommunication and/or IoT, a plus.

• Experience with data visualization and dashboard creation is a plus
• Ability to work independently with high energy, enthusiasm and persistence

We provide equal employment opportunities without regard to race, color, gender, sexual orientation, transgender status, gender identity and/or expression, marital status, pregnancy, parental status, religion, political opinion, nationality, ethnic background, social origin, social status, indigenous status, disability, age, union membership or employee representation and any other characteristic protected by local law or Ericsson s Code of Business Ethics. ,

Keyskills :
european works councilsbig datadata sciencewhite papersdeep learningspoken englishdata structurescomputer sciencemachine learningmodel validationequipment supplystream processingdata visualizationproduct innovation