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Job Description

We are looking for an experienced engineer to join our data science team, who will help us design, develop, and deploy machine learning models in production. You will develop robust models, prepare their deployment into production in a controlled manner, while providing appropriate means to monitor their performance and stability after deployment.

What You’ll Do will include (But not limited to):

• Preparing datasets needed to train and validate our machine learning models
• Anticipate and build solutions for problems that interrupt availability, performance, and stability in our systems, services, and products at scale.
• Defining and implementing metrics to evaluate the performance of the models, both for computing performance (such as CPU & memory usage) and for ML performance (such as precision, recall, and F1)
• Supporting the deployment of machine learning models on our infrastructure, including containerization, instrumentation, and versioning
• Supporting the whole lifecycle of our machine learning models, including gathering data for retraining, A/B testing, and redeployments
• Developing, testing, and evaluating tools for machine learning models deployment, monitoring, retraining.
• Working closely within a distributed team to analyze and apply innovative solutions over billions of documents
• Supporting solutions ranging from rule-bases, classical ML techniques  to the latest deep learning systems.
• Partnering with cross-functional team members to bring large scale data engineering solutions to production
• Communicating your approach and results to a wider audience through presentations

Your Qualifications:

• Demonstrated success with machine learning in a SaaS or Cloud environment, with hands–on knowledge of model creation and deployments in production at scale
• Good knowledge of traditional machine learning methods and neural networks
• Experience with practical machine learning modeling, especially on time-series forecasting, analysis, and causal inference.
• Experience with data mining algorithms and statistical modeling techniques for anomaly detection in time series such as clustering, classification, ARIMA, and decision trees is preferred.
• Ability to implement data import, cleansing and transformation functions at scale
• Fluency in Docker, Kubernetes
• Working knowledge of relational and dimensional data models with appropriate visualization techniques such as PCA.
• Solid English skills to effectively communicate with other team members

Due to the nature of the role, it would be nice if you have also:

• Experience with large datasets and distributed computing, especially with the Google Cloud Platform
• Fluency in at least one deep learning framework: PyTorch, TensorFlow / Keras
• Experience with No–SQL and Graph databases
• Experience working in a Colab, Jupyter, or Python notebook environment
• Some experience with monitoring, analysis, and alerting tools like New Relic, Prometheus, and the ELK stack
• Knowledge of Java, Scala or Go-Lang programming languages
• Familiarity with KubeFlow
• Experience with transformers, for example the Hugging Face libraries
• Experience with OpenCV

About Egnyte

In a content critical age, Egnyte fuels business growth by enabling content-rich business processes, while also providing organizations with visibility and control over their content assets. Egnyte’s cloud-native content services platform leverages the industry’s leading content intelligence engine to deliver a simple, secure, and vendor-neutral foundation for managing enterprise content across business applications and storage repositories. More than 16,000 customers trust Egnyte to enhance employee productivity, automate data management, and reduce file-sharing cost and complexity. Investors include Google Ventures, Kleiner Perkins, Caufield & Byers, and Goldman Sachs. For more information, visit www.egnyte.com

#LI-Remote

Responsibilities:

• Data science model review, run the code refactoring and optimization, containerization, deployment, versioning, and monitoring of its quality.
• Design and implement cloud solutions, build MLOps on the cloud (preferably AWS)
• Work with workflow orchestration tools like Kubeflow, Airflow, Argo, or similar tools
• Data science models testing, validation, and test automation.
• Communicate with a team of data scientists, data engineers, and architects, and document the processes.

Eligibility:

• Rich hands-on experience in writing object-oriented code using python
• Min 3 years of MLOps experience (Including model versioning, model and data lineage, monitoring, model hosting and deployment, scalability, orchestration, continuous learning, and Automated pipelines)
• Understanding of Data Structures, Data Systems, and software architecture
• Experience in using MLOps frameworks like Kubeflow, MLFlow, and Airflow Pipelines for building, deploying, and managing multi-step ML workflows based on Docker containers and Kubernetes.
• Exposure to deep learning approaches and modeling frameworks (PyTorch, Tensorflow, Keras, etc. )

This profile will include the following responsibilities:

- Develop Parsers for XML and JSON Data sources/feeds

- Write Automation Scripts for product development

- Build API Integrations for 3^rd Party product integration

- Perform Data Analysis

- Research on Machine learning algorithms

- Understand AWS cloud architecture and work with 3 party vendors for deployments

Responsibilities

1. Create data funnels to feed into models via web, structured and unstructured data
2. Maintain coding standards using  SDLC, Git, AWS deployments etc
3. Keep abreast of developments in the field
4. Deploy models in production and monitor them
5. Documentations of processes and logic
6. Take ownership of the solution from code to deployment and performance

Roles and Responsibilities

• Managing available resources such as hardware, data, and personnel so that deadlines are met.
• Analyzing the ML and Deep Learning algorithms that could be used to solve a given problem and ranking them by their success probabilities
• Exploring 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
• Defining validation framework and establish a process to ensure acceptable data quality criteria are met
• Supervising the data acquisition and partnership roadmaps to create stronger product for our customers.
• Defining feature engineering process to ensure usage of meaningful features given the business constraints which may vary by market
• Device self-learning strategies through analysis of errors from the models
• Understand business issues and context, devise a framework for solving unstructured problems and articulate clear and actionable solutions underpinned by analytics.
• Manage multiple projects simultaneously while demonstrating business leadership to collaborate & coordinate with different functions to deliver the solutions in a timely, efficient and effective manner.
• Manage project resources optimally to deliver projects on time; drive innovation using residual resources to create strong solution pipeline; provide direction, coaching & training, feedbacks to project team members to enhance performance, support development and encourage value aligned behaviour of the project team members; Provide inputs for periodic performance appraisal of project team members.

Preferred Technical & Professional expertise

• Undergraduate Degree in Computer Science / Engineering / Mathematics / Statistics / economics or other quantitative fields
• At least 2+ years of experience of managing Data Science projects with specializations in Machine Learning
• In-depth knowledge of cloud analytics tools.
• Able to drive Python Code optimization; ability review codes and provide inputs to improve the quality of codes
• Ability to evaluate hardware selection for running ML models for optimal performance
• Up to date with Python libraries and versions for machine learning; Extensive hands-on experience with Regressors; Experience working with data pipelines.
• Deep knowledge of math, probability, statistics and algorithms; Working knowledge of Supervised Learning, Adversarial Learning and Unsupervised learning
• Deep analytical thinking with excellent problem-solving abilities
• Strong verbal and written communication skills with a proven ability to work with all levels of management; effective interpersonal and influencing skills.
• Ability to manage a project team through effectively allocation of tasks, anticipating risks and setting realistic timelines for managing the expectations of key stakeholders
• Strong organizational skills and an ability to balance and handle multiple concurrent tasks and/or issues simultaneously.
• Ensure that the project team understand and abide by compliance framework for policies, data, systems etc. as per group, region and local standards

4-6 years of total experience in data warehousing and business intelligence

3+ years of solid Power BI experience (Power Query, M-Query, DAX, Aggregates)

2 years’ experience building Power BI using cloud data (Snowflake, Azure Synapse, SQL DB, data lake)

Strong experience building visually appealing UI/UX in Power BI

Understand how to design Power BI solutions for performance (composite models, incremental refresh, analysis services)

Experience building Power BI using large data in direct query mode

Expert SQL background (query building, stored procedure, optimizing performance)

• You hold an MS/Ph.D. degree in a STEM domain and 5+ years in a relevant position
• You share your ideas and continuously improve yourself and the team around you.
• Experienced in building and scaling data teams across multiple locations and domains.
• You have a good understanding of evolving an organization’s culture based on analytics and data insights
• Natural and comfortable leader, have excellent problem-solving, organizational, and analytical skills
• Passionate about data improving business and engineering practices like continuous delivery, traceability, and observability
• Strong communication skills, high integrity, and great attention to detail

You’ll get to work with:

• Consumer-facing, as well as core platform, finance, and distribution business units
• Marketing and product teams, across to our engineering teams
• Modern infrastructure (Kubernetes, AWS, GCP)

What we offer

• We offer you a chance to be part of a truly amazing journey in a company that sets very high targets and works hard to achieve them. You will be able to work with smart, motivated, and engaged co-workers from all over the world, in an intense and very energetic environment. This leads to you having a tangible impact on the way that we operate and expand our business.

Some of the highlights of the package include:

• Strong technical culture of continuous innovation and improvement
• Chance to become a shareholder of Gelato!
• Flexible festive holidays, swap days off according to your values and beliefs.
• Work at one of our hub city offices or even remotely
• And much more!

ML Engineer-Analyst/ Senior Analyst

Job purpose:

To design and develop machine learning and deep learning systems. Run machine learning tests andexperiments and implementing appropriate ML algorithms. Works cross-functionally with the Data Scientists, Software application developers and business groups for the development of innovative ML models. Use Agile experience to work collaboratively with other Managers/Owners in geographically distributed teams.

Accountabilities:

• Work with Data Scientists and Business Analysts to frame problems in a business context. Assist all the processes from data collection, cleaning, and preprocessing, to training models and deploying them to production.

• Understand business objectives and developing models that help to achieve them, along with metrics to track their progress.

• Explore and visualize data to gain an understanding of it, then identify differences in data distribution that could affect performance when deploying the model in the real world.

• Define validation strategies, preprocess or feature engineering to be done on a given dataset and data augmentation pipelines.

• Analyze the errors of the model and design strategies to overcome them.
• Collaborate with data engineers to build data and model pipelines, manage the infrastructure and data pipelines needed to bring code to production and demonstrate end-to-end understanding of applications (including, but not limited to, the machine learning algorithms) being created.

Qualifications & Specifications

• Bachelor's degree in Engineering /Computer Science/ Math/ Statistics or equivalent. Master's degree in relevant specification will be first preference

• Experience of machine learning algorithms and libraries
• Understanding of data structures, data modeling and software architecture.
• Deep knowledge of math, probability, statistics and algorithms
• Experience with machine learning platforms such as Microsoft Azure, Google Cloud, IBM Watson, and Amazon

• Big data environment: Hadoop, Spark
• Programming languages: Python, R, PySpark
• Supervised & Unsupervised machine learning: linear regression, logistic regression, k-means

clustering, ensemble models, random forest, svm, gradient boosting

• Sampling data: bagging & boosting, bootstrapping
• Neural networks: ANN, CNN, RNN related topics
• Deep learning: Keras, Tensorflow
• Experience with AWS Sagemaker deployment and agile methodology

We are building a global content marketplace that brings companies and content

creators together to scale up content creation processes across 50+ content verticals and 150+ industries. Over the past 2.5 years, we’ve worked with companies like India Today, Amazon India, Adobe, Swiggy, Dunzo, Businessworld, Paisabazaar, IndiGo Airlines, Apollo Hospitals, Infoedge, Times Group, Digit, BookMyShow, UpGrad, Yulu, YourStory, and 350+ other brands.
Our mission is to become the world’s largest content creation and distribution platform for all kinds of content creators and brands.

Our Team

We are a 25+ member company and is scaling up rapidly in both team size and our ambition.

If we were to define the kind of people and the culture we have, it would be -

a) Individuals with an Extreme Sense of Passion About Work

b) Individuals with Strong Customer and Creator Obsession

c) Individuals with Extraordinary Hustle, Perseverance & Ambition

We are on the lookout for individuals who are always open to going the extra mile and thrive in a fast-paced environment. We are strong believers in building a great, enduring

a company that can outlast its builders and create a massive impact on the lives of our

employees, creators, and customers alike.

Our Investors

We are fortunate to be backed by some of the industry’s most prolific angel investors - Kunal Bahl and Rohit Bansal (Snapdeal founders), YourStory Media. (Shradha Sharma); Dr. Saurabh Srivastava, Co-founder of IAN and NASSCOM; Slideshare co-founder Amit Ranjan; Indifi's Co-founder and CEO Alok Mittal; Sidharth Rao, Chairman of Dentsu Webchutney; Ritesh Malik, Co-founder and CEO of Innov8; Sanjay Tripathy, former CMO, HDFC Life, and CEO of Agilio Labs; Manan Maheshwari, Co-founder of WYSH; and Hemanshu Jain, Co-founder of Diabeto.
Backed by Lightspeed Venture Partners

Nactus is at forefront of education reinvention, helping educators and learner’s community at large through innovative solutions in digital era. We are looking for an experienced AI specialist to join our revolution using the deep learning, artificial intelligence.  This is an excellent opportunity to take advantage of emerging trends and technologies to a real-world difference.

Role and Responsibilities

• Manage and direct research and development (R&D) and processes to meet the needs of our AI strategy.
• Understand company and client challenges and how integrating AI capabilities can help create educational solutions.
• Analyse and explain AI and machine learning (ML) solutions while setting and maintaining high ethical standards.

Skills Required

• Knowledge of algorithms, object-oriented and functional design principles
• Demonstrated artificial intelligence, machine learning, mathematical and statistical modelling knowledge and skills.
• Well-developed programming skills – specifically in SAS or SQL and other packages with statistical and machine learning application, e.g. R, Python
• Experience with machine learning fundamentals, parallel computing and distributed systems fundamentals, or data structure fundamentals
• Experience with C, C++, or Python programming
• Experience with debugging and building AI applications.
• Robustness and productivity analyse conclusions.
• Develop a human-machine speech interface.
• Verify, evaluate, and demonstrate implemented work.
• Proven experience with ML, deep learning, Tensorflow, Python