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Staff Machine Learning Engineer Job Opening In Bengaluru – Now Hiring Bazaarvoice


Job description


Staff Machine Learning Engineer

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

The Role:
We are seeking a highly experienced and strategic Staff Machine Learning Engineer to join our AI & Data Science team.

This is a senior-most individual contributor role where you will be responsible for owning the end-to-end Machine Learning Development Lifecycle (MDLC).

You will architect, build, and deploy production-grade, scalable ML systems that transform massive volumes of user-generated content into actionable insights for our customers.

This position requires a proven track record of solving complex, unstructured data challenges and a deep expertise in building robust, high-performance systems on the AWS cloud.

What You'll Do (Responsibilities)Architect and Innovate:
Lead the design, development, and deployment of complex, production-grade ML systems and data pipelines, particularly for Natural Language Processing (NLP) and Generative AI applications.
Solve High-Complexity Problems: Serve as a domain expert in the application of AI to solve core business challenges, including sentiment analysis, content moderation, product recommendations, and personalized search.
Technical Leadership: Drive innovation by identifying and addressing high-impact technical challenges and long-standing technical debt within our ML and data infrastructure.
Mentorship and Standards: Provide technical mentorship to other engineers on the team and beyond, raising the bar for engineering excellence, maintainability, and best practices across the organization.
Cross-Functional Collaboration: Collaborate closely with Data Scientists, Product Managers, and other engineering teams to translate complex business requirements into robust, data-driven ML solutions.
Operational Excellence: Implement and oversee MLOps practices, including automated CI/CD pipelines, model monitoring, and governance, to ensure our systems are reliable, reproducible, and performant at scale.
Observability: Implement robust observability frameworks to proactively detect and diagnose issues like model drift, data quality anomalies, and performance degradation in production.

What You'll Bring (Required Skills & Experience)Experience:
Minimum of 8+ years of experience in Machine Learning Engineering, Applied Machine Learning, or a related field, with a proven track record of building and maintaining production models.
MLOps & AWS: Expert proficiency with the AWS ecosystem for MLOps, including a deep understanding of how to architect solutions using key services like Amazon SageMaker, S3, AWS Step Functions, AWS CloudFormation, Amazon CloudWatch, Amazon Managed Streaming for Apache Kafka (MSK), and Amazon Bedrock.
Technical Expertise:Deep expertise in building and deploying scalable solutions for NLP, including experience with challenges such as sarcasm detection, polysemy, and managing multilingual data.
Experience with a variety of ML algorithms and models, including traditional supervised and unsupervised learning, deep learning, and modern Generative AI techniques (e.g., LLMs, RAG, Prompt Engineering).Proficiency with ML frameworks and libraries such as PyTorch, TensorFlow, and scikit-learn, with an ability to adapt and tune open-source or pre-trained models.
Software Engineering & Observability: A strong understanding of core software engineering principles, including design patterns, data structures, testing, security, and version control.

Experience with continuous integration (CI/CD) and regression testing.

You should be able to apply model observability practices for faster issue detection and root cause analysis.
Problem-Solving: The ability to translate complex business problems into viable technical solutions and communicate findings to stakeholders in non-technical terms.
Software Engineering: A strong understanding of software engineering principles, including design patterns, data structures, testing, security, and version control.
#LI-Hybrid

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 its 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 were 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 whats 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 worlds smartest network of consumers, brands, and retailers.

As part of our hiring process, a background check will be conducted.

This will be done with your consent and will only include information relevant to the job.





PI6aebac5fc02b-30511-38401303

Required Skill Profession

Computer Occupations


  • Job Details

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The Work Culture

An organization's rules and standards set how people should be treated in the office and how different situations should be handled. The work culture at Bazaarvoice adheres to the cultural norms as outlined by Expertini.

The fundamental ethical values are:

1. Independence

2. Loyalty

3. Impartiapty

4. Integrity

5. Accountabipty

6. Respect for human rights

7. Obeying India laws and regulations

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Interview Tips for Staff Machine Learning Engineer Job Success

Bazaarvoice interview tips for Staff Machine Learning Engineer

Here are some tips to help you prepare for and ace your Staff Machine Learning Engineer job interview:

Before the Interview:

Research: Learn about the Bazaarvoice's mission, values, products, and the specific job requirements and get further information about

Other Openings

Practice: Prepare answers to common interview questions and rehearse using the STAR method (Situation, Task, Action, Result) to showcase your skills and experiences.

Dress Professionally: Choose attire appropriate for the company culture.

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Plan Your Commute: Allow ample time to arrive on time and avoid feeling rushed.

During the Interview:

Be Punctual: Arrive on time to demonstrate professionalism and respect.

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Confidence and Enthusiasm: Project a positive attitude and show your genuine interest in the opportunity.

Answer Thoughtfully: Listen carefully, take a moment to formulate clear and concise responses. Highlight relevant skills and experiences using the STAR method.

Ask Prepared Questions: Demonstrate curiosity and engagement with the role and company.

Follow Up: Send a thank-you email to the interviewer within 24 hours.

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Turn Off Phone: Avoid distractions during the interview.

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To prepare for your Staff Machine Learning Engineer interview at Bazaarvoice, research the company, understand the job requirements, and practice common interview questions.

Highlight your leadership skills, achievements, and strategic thinking abilities. Be prepared to discuss your experience with HR, including your approach to meeting targets as a team player. Additionally, review the Bazaarvoice's products or services and be prepared to discuss how you can contribute to their success.

By following these tips, you can increase your chances of making a positive impression and landing the job!

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