Staff Machine Learning Engineer

Bazaarvoice is seeking a highly experienced and strategic Staff Machine Learning Engineer to join our AI & Data Science team in Bengaluru. This senior individual contributor role focuses on end-to-end Machine Learning Development Lifecycle (MDLC). You will be instrumental in architecting, building, and deploying scalable, production-grade ML systems that convert massive volumes of user-generated content into actionable insights for our clients. Success in this position requires a proven ability to tackle complex, unstructured data challenges and deep expertise in developing robust, high-performance systems within the AWS cloud environment.

Responsibilities:

• Architect and Innovate: Lead the design, development, and deployment of complex, production-grade ML systems and data pipelines, with a focus on Natural Language Processing (NLP) and Generative AI applications.
• Solve High-Complexity Problems: Act as a domain expert in applying AI to address core business challenges, including sentiment analysis, content moderation, product recommendations, and personalized search.
• Technical Leadership: Drive innovation by identifying and resolving high-impact technical challenges and long-standing technical debt within our ML and data infrastructure.
• Mentorship and Standards: Provide technical mentorship to team members and the wider engineering organization, elevating engineering excellence, maintainability, and best practices.
• Cross-Functional Collaboration: Work 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 system reliability, reproducibility, and performance at scale.
• Observability: Implement comprehensive observability frameworks to proactively detect and diagnose issues such as model drift, data quality anomalies, and performance degradation in production environments.

Required Skills & Experience:

• Experience: Minimum of 8+ years in Machine Learning Engineering, Applied Machine Learning, or a related field, with a demonstrated history of building and maintaining production models.
• MLOps & AWS Expertise: Expert proficiency in the AWS ecosystem for MLOps, including in-depth knowledge of architecting solutions using services like Amazon SageMaker, S3, AWS Step Functions, AWS CloudFormation, Amazon CloudWatch, Amazon Managed Streaming for Apache Kafka (MSK), and Amazon Bedrock.
• Technical Specialization: Deep expertise in building and deploying scalable NLP solutions, including experience with challenges like sarcasm detection, polysemy, and managing multilingual data.
• ML Model Proficiency: Experience with a wide range of ML algorithms and models, including traditional supervised/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 the ability to adapt and fine-tune open-source or pre-trained models.
• Software Engineering & Observability: Strong understanding of core software engineering principles, including design patterns, data structures, testing, security, and version control. Experience with CI/CD and regression testing. Ability to apply model observability practices for rapid issue detection and root cause analysis.
• Problem-Solving Skills: Proven ability to translate complex business problems into viable technical solutions and effectively communicate findings to stakeholders, including non-technical audiences.

This is a hybrid role.