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Designation: Project Manager, Engineering manager, Group Engineering Manager

Roles & Responsibilities:

Relevant experience working in client facing role with software product companies for product roadmap, delivery management

Knowledge of Agile framework ADO JIRA Microsoft Project

Ability of work on multiple projects, driving coordination between engineering test networking.

Ability to write functional design document, use cases or orchestration diagrams

Previous experience with Microsoft, Amazon AWS or cloud technology is huge plus

Skills: 

Project management, Client communication, People management.

If from a Cloud domain -- added advantage. 

On Tech skills: we can look for past experience in C++ or Java or Python or Databases. 

Client exposure: Projects with Microsoft or any Chinese, Japanese or US based clients.

Minimum of bachelor’s degree in Engineering (preferably in Computer Science Engineering).

Experience of architecture design, implementation, and support of highly distributed setups (ensure high availability and reliability).

Experience in migrating on-premises workloads to the public cloud.   

Strong understanding of cloud, automation, and infrastructure.

Strong troubleshooting skills for cloud and automation.

Strong experience in implementing orchestration and automation. 

Experience in successfully deploying cloud in an enterprise. 

Experience with versioning tools such as GIT and DevOps.

Experienced in scripting and coding a must.

Deep technical experience in IaaS and PaaS. 

Ability to connect on-premises technology to cloud offerings.

Excellent verbal and written communication skills. 

Proven track record of driving decisions collaboratively and follow through.

Have excellent technical leadership.   

Self-starter and motivator and ability to work in team and solo.

Years of Experience Required:- 1 - 5 Years 

Cloud/ Edge Computing/Azure/AWS/Alibaba/Huawei 

Web Services/ API knowledge. 

CSS, JavaScript, XML, jQuery 

Angular, React, Node.js, Express.js  Frameworks

Good to have knowledge of Backend Languages like Python

Databases MySQL, MongoDB), Web servers (e.g. Apache) and UI/UX design

Job Description:

As a data scientist you will design and implement data analytics projects, support and maintain data pipelines, and provide expertise and best practices regarding data systems for stakeholders across the company. 

Data scientists are expected to have an in-depth understanding of data systems as well as expertise with the tools needed to debug and diagnose issues. You will work closely with the 

Engineering Managers, Product managers and other software engineers to advance projects and meet objectives.

Responsibilities:

Design, prototype and deploy data solutions at scale using industry best practices.

Performs ETL, ELT operations and administration of data and systems securely and in accordance with enterprise data governance standards.

Conduct complex data analysis by selecting features, building and optimizing data models using machine learning and AI techniques.

Create scripts and programs to automate data operations. 

Display expertise with tools needed to debug and diagnose issues

Provide guidance to other engineers and workflow developers

Work well with technical leads, incorporating feedback as needed

Skills:

Minimum 2+ years of experience in AI/ML related projects

Highly proficient with two or more languages, including Python, PySpark, SQL, and experience 

with and tools like DataBricks, Azure ML and Spark

Excellent understanding of machine learning algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc.

Highly skilled in statistical analysis, quantitative analytics, forecasting/predictive analytics, deep learning, simulation and optimization algorithms.

Detailed knowledge of data warehouse technical architectures, infrastructure components, ETL/ ELT and reporting/analytic tools. 

Proficiency with SQL, NoSQL and Cloud based DW such as Azure Synapse, Google Big Query, Snowflake etc.

Experience in Big Data Distributed ecosystems (Hadoop, SPARK, Hive & Delta Lake) Source control using Git

Adaptable to Windows, Linux, and container-based deployment environments

Good to have:

Professional certifications (Azure, AWS, GCP)

Proficiency with Azure Stack.

Experience with container management frameworks such as Docker, Kubernetes, etc.