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Senior MlOps

 About Bazaarvoice

 

At Bazaarvoice, we create smart shopping experiences. Through our expansive global network, product-passionate community & enterprise technology, we connect thousands of brands and retailers with billions of consumers. Our solutions enable brands to connect with consumers and collect valuable user-generated content, at an unprecedented scale. This content achieves global reach by leveraging our extensive and ever-expanding retail, social & search syndication network. And we make it easy for brands & retailers to gain valuable business insights from real-time consumer feedback with intuitive tools and dashboards. The result is smarter shopping: loyal customers, increased sales, and improved products.

 

The problem we are trying to solve : Brands and retailers struggle to make real connections with consumers. It's a challenge to deliver trustworthy and inspiring content in the moments that matter most during the discovery and purchase cycle. The result? Time and money spent on content that doesn't attract new consumers, convert them, or earn their long-term loyalty.

 

Our brand promise : closing the gap between brands and consumers.

 

Founded in 2005, Bazaarvoice is headquartered in Austin, Texas with offices in North America, Europe, Asia and Australia.

 

It’s official: Bazaarvoice is a Great Place to Work in the US , Australia, India, Lithuania, France, Germany and the UK!


Key Responsibilities


Design, implement, and maintain robust MLOps platforms and tooling for both batch and streaming ML pipelines.Develop and manage monitoring and observability solutions for ML systems.Follow DevOps practices, including CI/CD pipelines and Infrastructure as Code (IaC).Implement cloud-based solutions on AWS (or similar public cloud providers).Collaborate with ML Engineers and Data Scientists to develop, train, and deploy machine learning models.Engage in feature engineering and model optimization to improve ML system performance.Participate in the full ML lifecycle, from data preparation to model deployment and monitoring.Optimize and refactor existing systems for improved performance and reliability.


Participate in technical initiatives and best practices in both MLOps and ML Engineering.

Required Skills and Experience

Strong Python Proficiency:

Excellent skills for developing, deploying, and maintaining our machine learning systems.

Language Versatility: 3+ Years of Experience with statically-typed or JVM languages. Willingness to learn Scala is highly desirable.

Cloud Engineering Skills: 3+ years experience with Cloud Platforms & Services, ideally AWS (e.g., Lambda, ECS, ECR, CloudWatch, MSK, SNS, SQS).

Infrastructure as Code: Beginner- or intermediate-level proficiency in IaC, particularly Terraform.

Kubernetes Expertise: 3+ years of hands-on experience with managing clusters and deploying services.

Data Orchestration: 3+ years of Experience with ML orchestration tools (e.g., Flyte, Airflow, Kubeflow, Luigi, or Prefect).

CI/CD: 3+ Years of Expertise in pipelines, especially GitHub Actions and Jenkins.

Networking: Knowledge of concepts and implementation.

Streaming: Experience with Kafka and other streaming technologies.

ML Monitoring: Familiarity with observability tools (e.g., Arize AI, Weights and Biases).

NLP/LLMs: Experience with NLP, LLMs, and RAG systems in production, or strong desire to learn.

CLI & Shell Scripting: Proficiency in scripting and command-line tools.

APIs: Experience with deploying and managing production APIs.

Software Engineering Best-Practices: Knowledge of industry standards and practices.

Preferred Qualifications

AWS AI Services: Hands-on experience with AWS SageMaker and/or AWS Bedrock.

Data Processing: Experience with high-volume, unstructured data processing.

ML Applications: Familiarity with NLP, Computer Vision, and traditional ML applications.

System Migration: Previous work in refactoring and migrating complex systems.

AWS Certification: AWS Solution Architect Professional or Associate certification.Personal QualitiesPassionate about building developer-friendly platforms and tools.Thrives in a terminal-based development environment.Enthusiastic about creating production-grade, robust, reliable, and performant systems.Not afraid to dive into and improve complex existing solutions.Team player who works well with ML Engineers, Data Scientists, and management.Good problem-solving and communication skills.



#LI-Hybrid#LI-PC1


Why join Bazaarvoice?

 

Customer is key

We see our own success through our customers’ outcomes.  

We approach every situation with a customer first mindset.

 

Transparency & Integrity Builds Trust

We believe in the power of authentic feedback because it’s in our DNA. 

We do the right thing when faced with hard choices. Transparency and trust accelerate our collective performance.

 

Passionate Pursuit of Performance

Our energy is contagious, because we hire for passion, drive & curiosity. 

We love what we do, and because we’re laser focused on our mission.

 

Innovation over Imitation

We seek to innovate as we are not content with the status quo. 

We embrace agility and experimentation as an advantage.

 

Stronger Together

We bring our whole selves to the mission and find value in diverse perspectives. 

We champion what’s best for Bazaarvoice before individuals or teams.  

As a stronger company we build a stronger community.

 

Commitment to diversity and inclusion

 

Bazaarvoice provides equal employment opportunities (EEO) to all team members and applicants according to their experience, talent, and qualifications for the job without regard to race, color, national origin, religion, age, disability, sex (including pregnancy, gender stereotyping, and marital status), sexual orientation, gender identity, genetic information, military/veteran status, or any other category protected by federal, state, or local law in every location in which the company has facilities. Bazaarvoice believes that diversity and an inclusive company culture are key drivers of creativity, innovation and performance. Furthermore, a diverse workforce and the maintenance of an atmosphere that welcomes versatile perspectives will enhance our ability to fulfill our vision of creating the world’s smartest network of consumers, brands, and retailers.

Average salary estimate

$120000 / YEARLY (est.)
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$100000K
$140000K

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What You Should Know About Senior MlOps, Bazaarvoice

Are you ready to take your career to new heights as a Senior MLOps at Bazaarvoice in Bengaluru? At Bazaarvoice, we believe in creating smarter shopping experiences that connect brands with consumers in meaningful ways. In this exciting role, you’ll be responsible for designing and maintaining robust MLOps platforms and tooling for both batch and streaming ML pipelines. Your expertise in Python will be essential as you collaborate with talented ML Engineers and Data Scientists to develop, train, and deploy cutting-edge machine learning models. You will implement cloud-based solutions using AWS while ensuring that best practices in DevOps, CI/CD pipelines, and Infrastructure as Code are followed. This is not just a job; it's an opportunity to engage in a dynamic environment where innovation is encouraged. You'll also have the chance to optimize and refactor existing systems, bringing your passion for tech to life. At Bazaarvoice, your contributions will shape the future of smart commerce, driving enhanced customer experiences through intelligent feedback and insights. Come join us and be part of a team that thrives on collaboration, creativity, and a customer-first mindset. With a focus on diversity and inclusion, Bazaarvoice is not only a great place to work but also a place where you can bring your whole self to the mission and make a real impact. Let’s innovate together!

Frequently Asked Questions (FAQs) for Senior MlOps Role at Bazaarvoice
What responsibilities does a Senior MLOps have at Bazaarvoice?

As a Senior MLOps at Bazaarvoice, you'll be tasked with designing and maintaining MLOps platforms, developing monitoring solutions, and collaborating closely with ML Engineers and Data Scientists. Your role will include optimizing existing systems and implementing cloud solutions on AWS.

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What qualifications are required for the Senior MLOps position at Bazaarvoice?

To be considered for the Senior MLOps role at Bazaarvoice, candidates should have strong Python proficiency, experience with cloud platforms (preferably AWS), hands-on Kubernetes experience, and familiarity with ML orchestration tools. A passion for developing reliable systems is crucial.

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How does Bazaarvoice support diversity in the workplace for the Senior MLOps role?

Bazaarvoice is committed to creating an inclusive environment that celebrates diverse perspectives. Applicants for the Senior MLOps position can expect equal employment opportunities and a culture that values creativity and innovation.

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What kind of projects will I work on as a Senior MLOps at Bazaarvoice?

In the Senior MLOps role at Bazaarvoice, you'll work on exciting projects involving machine learning model deployment and monitoring. You'll also engage in refactoring and system migration projects, ensuring systems are both robust and scalable.

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What technologies does Bazaarvoice use for their MLOps?

Bazaarvoice utilizes a variety of technologies for their MLOps, including Python, AWS services (like Lambda and ECS), Kubernetes, and diverse ML orchestration tools such as Airflow and Flyte.

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What personal qualities are desired in a Senior MLOps candidate at Bazaarvoice?

Candidates for the Senior MLOps position at Bazaarvoice should demonstrate a strong passion for tech, excellent problem-solving skills, and a collaborative spirit, as the role involves working closely with various teams to innovate and optimize systems.

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Is remote work an option for the Senior MLOps position at Bazaarvoice?

Bazaarvoice promotes a hybrid work model, allowing flexibility and a work environment that suits the needs of their employees, making it easier to balance personal and professional commitments.

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Common Interview Questions for Senior MlOps
Can you describe your experience with MLOps platforms and tools?

When answering this question, discuss specific platforms you've worked with, such as Kubernetes or ML orchestration tools, and elaborate on how you've implemented them in past projects.

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How do you ensure the reliability of ML systems?

You might want to emphasize your experience with monitoring solutions, regular updates, and applying best practices in CI/CD. Providing examples of past challenges and how you resolved them will strengthen your answer.

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What cloud services have you used in your previous MLOps roles?

Be prepared to mention specific cloud services, especially those from AWS, like Lambda, ECS, or CloudWatch. Discuss how these services have aided in deploying and managing ML pipelines.

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How would you handle a performance issue in a deployed ML model?

Outline your approach to troubleshooting, optimizations you could implement, and the importance of engaging with your team to discuss potential solutions.

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Describe a time you collaborated with data scientists and engineers.

Give an example of a project where collaboration was key. Highlight how you communicated effectively and merged technical aspects from both roles to achieve a successful outcome.

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What programming languages are you proficient in and how do they apply to MLOps?

Focus on your proficiency in Python and any other relevant languages like Scala. Illustrate how you've used these within the MLOps lifecycle to develop reliable solutions.

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What CI/CD practices do you follow in your MLOps work?

Discuss your experience implementing CI/CD pipelines, the tools you’ve used (like GitHub Actions or Jenkins), and how these practices improve deployment workflows.

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Can you explain the process of model deployment from development to monitoring?

Provide a comprehensive yet concise overview of the ML lifecycle, emphasizing each stage and the importance of monitoring to ensure models maintain performance post-deployment.

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What strategies do you use for feature engineering?

Talk about specific techniques and tools you prefer for feature engineering, and perhaps share a success story where your features significantly improved model performance.

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How do you keep up with developments in ML technology?

Express your dedication to continual learning through online courses, following leading industry publications, and participating in relevant forums to stay updated on trends and technologies in MLOps.

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Our mission is to build smarter shopper experiences across the entire customer journey. Whether it’s getting your user-generated content program started on day one or expanding global multichannel strategies, we’re here to be a true strategic part...

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Full-time, hybrid
DATE POSTED
November 24, 2024

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