QJD/001/ ML Ops Engineer/a/2024
Position Title: ML Ops Engineer
Location: Bangalore / Lucknow
Work Type: Hybrid
Contact: [email protected]
Call: 9741127757
About Quation
Welcome to Quation, where data transforms into the currency of informed decisions. We are more than a data analytics company; we are the architects of your data-driven success. Our commitment to precision, innovation, and industry transformation sets us apart. At Quation, we embody the essence of “Powering Data, Empowering Decisions.” We are driven by the belief that data, when harnessed intelligently, has the power to revolutionize industries, driving growth and efficiency. Join us in revolutionizing the world of data analytics, AI, and ML.
Job Description
As an ML Ops Engineer at Quation, you will manage the deployment and integration of machine learning models into production. You’ll ensure end-to-end deployment of analytics solutions, manage large-scale data handling, and provide essential data visualizations. You will also collaborate with customer success teams to enhance usability, streamline analytical workflows, and simplify technical concepts for non-technical stakeholders.
Years of Experience
• 3 – 5 years of relevant experience in ML Ops, data deployment, and analytics project management.
Budget
Compensation: Competitive and aligned with industry standards.
Key Responsibilities
• Lead and oversee timely completion of data analytics projects as part of the analytics implementation process.
• Deploy analytics solutions, including applications and dashboards, accurately and within client timelines.
• Assess business requirements and design analytical dashboards on cloud platforms for retail and digital brands.
• Manage the full data science life cycle: data collection, preparation, modeling, evaluation, and scaled deployment.
• Handle data at scale, deploying machine learning models with visualization capabilities in production.
• Collaborate with customer success and category teams to identify and address client usability challenges.
• Communicate technical concepts in a simplified manner to business stakeholders.
Qualifications
• Deploy analytics solutions, including applications and dashboards, accurately and within client timelines.
• Assess business requirements and design analytical dashboards on cloud platforms for retail and digital brands.
• Manage the full data science life cycle: data collection, preparation, modeling, evaluation, and scaled deployment.
• Handle data at scale, deploying machine learning models with visualization capabilities in production.
• Collaborate with customer success and category teams to identify and address client usability challenges.
• Communicate technical concepts in a simplified manner to business stakeholders.
Qualifications
Basic Qualifications
• Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field.
• Proficiency in Python and associated libraries such as NumPy and Pandas.
• Solid understanding of cloud computing, especially with AWS.
• Proficiency in Python and associated libraries such as NumPy and Pandas.
• Solid understanding of cloud computing, especially with AWS.
Preferred Qualifications
• 3-5+ years of experience in managing data visualization and analytics solutions on cloud platforms.
• In-depth knowledge of AWS services, including AWS Glue, Athena, and EC2.
• Experience with building and deploying data visualization dashboards using AWS Quicksight, Tableau, Streamlit, or PowerBI.
• Strong background in adhering to AWS architecture, working with diverse data sources, and constructing ETLs.
• Exposure to unstructured data and familiarity with REST APIs, Docker, and SQL.
• Demonstrated capability in quickly learning new technologies, creating Proof of Concepts (POCs), and assessing solution feasibility.
Suggested Skills
• Technical Skills: Python, NumPy, Pandas, Flask, API integrations, distributed programming, web crawling.
• Cloud Expertise: AWS, including ETL tools like AWS Glue and AWS Athena, as well as Docker for containerization.
• Cloud Expertise: AWS, including ETL tools like AWS Glue and AWS Athena, as well as Docker for containerization.
• Database Knowledge: SQL proficiency, especially in cloud-based environments.
• Machine Learning Techniques: Skilled in regression, clustering, time series analysis, and both supervised and unsupervised learning methods.
• Visualization & Analytics Tools: Familiarity with AWS Quicksight, Streamlit, or similar tools.
• Domain Knowledge: Understanding of retail/e-commerce analytics and metrics.
• Machine Learning Techniques: Skilled in regression, clustering, time series analysis, and both supervised and unsupervised learning methods.
• Visualization & Analytics Tools: Familiarity with AWS Quicksight, Streamlit, or similar tools.
• Domain Knowledge: Understanding of retail/e-commerce analytics and metrics.
Preferred Skills
• Strong understanding of AWS and data analytics services.
• Experience in building and deploying dashboards using AWS Quicksight, Tableau, Streamlit, or similar platforms.
• Ability to work with large data sets, build ETLs, and design analytics solutions from scratch.
Notice Period
• Immediate to 30-day notice period candidates are preferred.
• Experience in building and deploying dashboards using AWS Quicksight, Tableau, Streamlit, or similar platforms.
• Ability to work with large data sets, build ETLs, and design analytics solutions from scratch.
Notice Period
• Immediate to 30-day notice period candidates are preferred.
Ideal Soft Skills
• Strong problem-solving abilities and effective communication with technical and non-technical teams.
• Proactive approach to identifying improvements for client experience and solution functionality.
• Proactive approach to identifying improvements for client experience and solution functionality.
General Recruitment Disclaimer
We are committed to building a diverse and inclusive workplace, and candidates of all backgrounds are encouraged to apply.
We are committed to building a diverse and inclusive workplace, and candidates of all backgrounds are encouraged to apply.
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