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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
at Accrete.ai
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. )
Role: Principal Software Engineer
We looking for a passionate Principle Engineer - Analytics to build data products that extract valuable business insights for efficiency and customer experience. This role will require managing, processing and analyzing large amounts of raw information and in scalable databases. This will also involve developing unique data structures and writing algorithms for the entirely new set of products. The candidate will be required to have critical thinking and problem-solving skills. The candidates must be experienced with software development with advanced algorithms and must be able to handle large volume of data. Exposure with statistics and machine learning algorithms is a big plus. The candidate should have some exposure to cloud environment, continuous integration and agile scrum processes.
Responsibilities:
• Lead projects both as a principal investigator and project manager, responsible for meeting project requirements on schedule
• Software Development that creates data driven intelligence in the products which deals with Big Data backends
• Exploratory analysis of the data to be able to come up with efficient data structures and algorithms for given requirements
• The system may or may not involve machine learning models and pipelines but will require advanced algorithm development
• Managing, data in large scale data stores (such as NoSQL DBs, time series DBs, Geospatial DBs etc.)
• Creating metrics and evaluation of algorithm for better accuracy and recall
• Ensuring efficient access and usage of data through the means of indexing, clustering etc.
• Collaborate with engineering and product development teams.
Requirements:
• Master’s or Bachelor’s degree in Engineering in one of these domains - Computer Science, Information Technology, Information Systems, or related field from top-tier school
• OR Master’s degree or higher in Statistics, Mathematics, with hands on background in software development.
• Experience of 8 to 10 year with product development, having done algorithmic work
• 5+ years of experience working with large data sets or do large scale quantitative analysis
• Understanding of SaaS based products and services.
• Strong algorithmic problem-solving skills
• Able to mentor and manage team and take responsibilities of team deadline.
Skill set required:
• In depth Knowledge Python programming languages
• Understanding of software architecture and software design
• Must have fully managed a project with a team
• Having worked with Agile project management practices
• Experience with data processing analytics and visualization tools in Python (such as pandas, matplotlib, Scipy, etc.)
• Strong understanding of SQL and querying to NoSQL database (eg. Mongo, Casandra, Redis
at TSG Global Services Private Limited
Exp-Min 10 Years
Location Mumbai
Sal-Nego
Powerbi, Tableau, QlikView,
Solution Architect/Technology Lead – Data Analytics
Role
Looking for Business Intelligence lead (BI Lead) having hands on experience BI tools (Tableau, SAP Business Objects, Financial and Accounting modules, Power BI), SAP integration, and database knowledge including one or more of Azure Synapse/Datafactory, SQL Server, Oracle, cloud-based DB Snowflake. Good knowledge of AI-ML, Python is also expected.
- You will be expected to work closely with our business users. The development will be performed using an Agile methodology which is based on scrum (time boxing, daily scrum meetings, retrospectives, etc.) and XP (continuous integration, refactoring, unit testing, etc) best practices. Candidates must therefore be able to work collaboratively, demonstrate good ownership, leadership and be able to work well in teams.
- Responsibilities :
- Design, development and support of multiple/hybrid Data sources, data visualization Framework using Power BI, Tableau, SAP Business Objects etc. and using ETL tools, Scripting, Python Scripting etc.
- Implementing DevOps techniques and practices like Continuous Integration, Continuous Deployment, Test Automation, Build Automation and Test-Driven Development to enable the rapid delivery of working code-utilizing tools like Git. Primary Skills
Requirements
- 10+ years working as a hands-on developer in Information Technology across Database, ETL and BI (SAP Business Objects, integration with SAP Financial and Accounting modules, Tableau, Power BI) & prior team management experience
- Tableau/PowerBI integration with SAP and knowledge of SAP modules related to finance is a must
- 3+ years of hands-on development experience in Data Warehousing and Data Processing
- 3+ years of Database development experience with a solid understanding of core database concepts and relational database design, SQL, Performance tuning
- 3+ years of hands-on development experience with Tableau
- 3+ years of Power BI experience including parameterized reports and publishing it on PowerBI Service
- Excellent understanding and practical experience delivering under an Agile methodology
- Ability to work with business users to provide technical support
- Ability to get involved in all the stages of project lifecycle, including analysis, design, development, testing, Good To have Skills
- Experience with other Visualization tools and reporting tools like SAP Business Objects.
at nymbleUP
Responsibilities
-
Create data funnels to feed into models via web, structured and unstructured data
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Maintain coding standards using SDLC, Git, AWS deployments etc
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Keep abreast of developments in the field
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Deploy models in production and monitor them
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Documentations of processes and logic
-
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
Lead Machine Learning Engineer
About IDfy
IDfy is ranked amongst the World's Top 100 Regulatory Technology companies for the last two years. IDfy's AI-powered technology solutions help real people unlock real opportunities. We create the confidence required for people and businesses to engage with each other in the digital world. If you have used any major payment wallets, digitally opened a bank account , have used a self-drive car, have played a real-money online game, or hosted people through AirBnB, it's quite likely that your identity has been verified through IDfy at some point.
About the team
- The machine learning team is a closely knit team responsible for building models and services that support key workflows for IDfy.
- Our models are critical for these workflows and as such are expected to perform accurately and with low latency. We use a mix of conventional and hand-crafted deep learning models.
- The team comes from diverse backgrounds and experience. We respect opinions and believe in honest, open communication.
- We work directly with business and product teams to craft solutions for our customers. We know that we are, and function as a platform and not a services company.
About the role
In this role you will:
- Work on all aspects of a production machine learning platform: acquiring data, training and building models, deploying models, building API services for exposing these models, maintaining them in production, and more.
- Work on performance tuning of models
- From time to time work on support and debugging of these production systems
- Work on researching the latest technology in the areas of our interest and applying it to build newer products and enhancement of the existing platform.
- Building workflows for training and production systems
- Contribute to documentation
While the emphasis will be on researching, building and deploying models into production, you will be expected to contribute to aspects mentioned above.
About you
- You are a seasoned machine learning engineer (or data scientist). Our ideal candidate is someone with 8+ years of experience in production machine learning.
Must Haves
- You should be experienced in framing and solving complex problems with the application of machine learning or deep learning models.
- Deep expertise in computer vision or NLP with the experience of putting it into production at scale.
- You have experienced that and understand that modelling is only a small part of building and delivering AI solutions and know what it takes to keep a high-performance system up and running.
- Managing a large scale production ML system for at least a couple of years
- Optimization and tuning of models for deployment at scale
- Monitoring and debugging of production ML systems
- An enthusiasm and drive to learn, assimilate and disseminate the state of the art research. A lot of what we are building will require innovative approaches using newly researched models and applications.
- Past experience of mentoring junior colleagues
- Knowledge of and experience in ML Ops and tooling for efficient machine learning processes
Good to Have
- Our stack also includes languages like Go and Elixir. We would love it if you know any of these or take interest in functional programming.
- We use Docker and Kubernetes for deploying our services, so an understanding of this would be useful to have.
- Experience in using any other platform, frameworks, tools.
Other things to keep in mind
- Our goal is to help a significant part of the world’s population unlock real opportunities. This is an opportunity to make a positive impact here, and we hope you like it as much as we do.
Life At IDfy
People at IDfy care about creating value. We take pride in the strong collaborative culture that we have built, and our love for solving challenging problems. Life at IDfy is not always what you’d expect at a tech start-up that’s growing exponentially every quarter. There’s still time and space for balance.
We host regular talks, events and performances around Life, Art, Sports, and Technology; continuously sparking creative neurons in our people to keep their intellectual juices flowing. There’s never a dull day at IDfy. The office environment is casual and it goes beyond just the dress code. We have no conventional hierarchies and believe in an open-door policy where everyone is approachable.
Role :
- Understand and translate statistics and analytics to address business problems
- Responsible for helping in data preparation and data pull, which is the first step in machine learning
- Should be able to do cut and slice data to extract interesting insights from the data
- Model development for better customer engagement and retention
- Hands on experience in relevant tools like SQL(expert), Excel, R/Python
- Working on strategy development to increase business revenue
Requirements:
- Hands on experience in relevant tools like SQL(expert), Excel, R/Python
- Statistics: Strong knowledge of statistics
- Should able to do data scraping & Data mining
- Be self-driven, and show ability to deliver on ambiguous projects
- An ability and interest in working in a fast-paced, ambiguous and rapidly-changing environment
- Should have worked on Business Projects for an organization, Ex: customer acquisition, Customer retention.
We are looking for an engineer with ML/DL background.
Ideal candidate should have the following skillset
1) Python
2) Tensorflow
3) Experience building and deploying systems
4) Experience with Theano/Torch/Caffe/Keras all useful
5) Experience Data warehousing/storage/management would be a plus
6) Experience writing production software would be a plus
7) Ideal candidate should have developed their own DL architechtures apart from using open source architechtures.
8) Ideal candidate would have extensive experience with computer vision applications
Candidates would be responsible for building Deep Learning models to solve specific problems. Workflow would look as follows:
1) Define Problem Statement (input -> output)
2) Preprocess Data
3) Build DL model
4) Test on different datasets using Transfer Learning
5) Parameter Tuning
6) Deployment to production
Candidate should have experience working on Deep Learning with an engineering degree from a top tier institute (preferably IIT/BITS or equivalent)