23 days old

AWS/Azure/Spark Cloud Consultant

Los Angeles, CA 90006


Schedule: Full-time


Organization: Accenture Digital


Travel: 80% (Monday - Thursday)





Job Description





As an Machine Learning Engineer, you will work closely with our data scientists and data engineers to design, build and deploy machine learning solutions for business usage in a production environment e.g. design self-running software to automate predictive models. The consultant will require deep hands on experience in all phases of a machine learning lifecycle to harden, build, deploy, scale, tune, monitor these machine learning models for operationalization of solutions using them. It is an exciting opportunity to apply your skills to deliver machine learning enabled business applications to industry problems at scale.


Responsibilities will include:





Design and package deployments of machine learning models for production environments working very closely with data scientists and data engineers.


Using experimentation code developed by Data Scientists as a basis, design and code production ready high-quality scalable application code for operational deployment.


Select appropriate datasets and data representation methods to enable quick turnaround time for feature engineering


Define and incorporate software engineering practices into model implementation code and deployments


Choose, design and use the right ML libraries, tools, programming languages, deployment infrastructures and frameworks for scaling experimented models in production.


Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world


Work with data scientists to train and retrain systems when necessary


Optimize model performance and calibrate on appropriate combination of cloud infrastructure and ML tools


Design and Implement APIs serving the model outcomes, integrating with business applications, incorporate business rules and obtaining feedback


Design and implement optimal model stores and collection of model metadata during training and operations


Design and Implement model monitoring and calibration solutions for performance tracking of production deployed models


Design and Implement containerized deployments of ML models to edge infrastructures


Design and Implement A/B testing approaches and standards for model evaluation




Basic Qualifications:





Bachelor's degree in Computer Science, Engineering, Statistics, Technical Science or 3+ years of IT/Software Development/Programming experience


Minimum 1-2 years experience of building and deploying production applications that embed Deep Learning and Machine Learning models, such as Linear Regression, Decision Trees, Random Forest etc.


Minimum 1-2 years experience in designing and deploying production ML models on a variety of cloud infrastructure, e.g. GPUs


Minimum 1-2 years experience setting and using model parameters and hyperparameters i.e. containerize and externalize to tune and scale the model for large datasets. Must have experience of deploying containerized models and ML pipelines using Docker, Kubernetes or equivalent technologies


Minimum 1-2 years experience in engineering models using frameworks such as TensorFlow, Keras, Theano, SciKit, PySpark etc.


Minimum 1-2 years experience of building, containerizing and deploying end to end automated ML pipelines using technologies like Spark and Cloud Native services in a large-scale production environment


Minimum 1-2 years experience of using Jupyterhub, Anaconda, Spyder, Databricks, Sagemaker for model engineering, deployments and monitoring.


Minimum 1-2 years experience with performance engineering of these models with very large-scale datasets on a large distributed infrastructure using technologies like Azure Databricks, AWS Sagemaker, AWS EMR etc.


Minimum 1-2 years experience using tools like MLFlow for managing end-to-end machine learning lifecycle for tracking experiments, packaging ML code and deploying models from various ML libraries to model serving and inference platforms


Experience with different cloud native (e.g. AWS, Azure, Google) as well as third party libraries that support learning models and algorithms.


Minimum 2+ years of strong programming skills in at least 2 languages from Python, Scala (and Spark), R on AWS, Azure, GCP or on-premise platforms.


Minimum 1-2 years experience working in an Agile environment


Understanding of advanced math skills (linear algebra, Bayesian statistics, group theory)


Deep understanding of software engineering and software architecture principles for building and deploying business critical applications.





Desired Also:


- Relevant certifications in AI, ML or Data Engineering from AWS, Microsoft or Google


- Understanding of all phases of a complete Data Science Life-cycle


- Strong Experience delivering scaled solutions that generated business outcomes and impacts


- Knowledge and Understanding of Cloud, Hybrid and On-Premise DevOps





Professional Skill Requirements:





Proven success in contributing to a team-oriented environment Proven ability to work creatively and analytically in a problem-solving environment Desire to work in an information systems environment Excellent leadership, communication (written and oral) and interpersonal skills.


All our consulting professionals receive comprehensive training covering business acumen, technical and professional skills development. You'll also have opportunities to hone your functional skills and expertise in an area of specialization. We offer a variety of formal and informal training programs at every level to help you acquire and build specialized skills faster. Learning takes place both on the job and through formal training conducted online, in the classroom, or in collaboration with teammates. The sheer variety of work we do, and the experience it offers, provide an unbeatable platform from which to build a career.


Proven ability to work creatively and analytically in a problem-solving environment


Desire to work in an information systems environment


Excellent communication (written and oral) and interpersonal skills


Excellent leadership and management skills


Demonstrated leadership in professional setting; either military or civilian


Demonstrated teamwork and collaboration in a professional setting; either military or civilian





Applicants for employment in the US must have work authorization that does not now or in the future require sponsorship of a visa for employment authorization in the United States and with Accenture (i.e., H1-B visa, F-1 visa (OPT), TN visa or any other non-immigrant status).





Candidates who are currently employed by a client of Accenture or an affiliated Accenture business may not be eligible for consideration.





Accenture is an EEO and Affirmative Action Employer of Females/Minorities/Veterans/Individuals with Disabilities.





Equal Employment Opportunity





All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by federal, state, or local law.





Job candidates will not be obligated to disclose sealed or expunged records of conviction or arrest as part of the hiring process.


Accenture is committed to providing veteran employment opportunities to our service men and women.




Categories

Posted: 2019-08-02 Expires: 2019-09-11

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AWS/Azure/Spark Cloud Consultant

Accenture
Los Angeles, CA 90006

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