Staff Machine Learning Engineer
Bazaarvoice1 year ago
Bengaluru, IndiaStaff+
Responsibilities
- Architect, develop, and deploy complex production-grade ML systems and data pipelines for NLP and generative AI applications.
- Apply AI to sentiment analysis, content moderation, product recommendations, personalized search, and other business challenges.
- Own technical challenges and technical debt across ML and data infrastructure.
- Implement MLOps practices including automated CI/CD pipelines, model monitoring, and governance.
- Build observability frameworks to detect model drift, data-quality anomalies, and production performance degradation.
- Mentor engineers and establish standards for engineering excellence, maintainability, and best practices.
- Collaborate with data scientists, product managers, and engineering teams to translate business requirements into ML solutions.
Requirements
- At least 8 years of experience in Machine Learning Engineering, Applied Machine Learning, or a related field, including building and maintaining production models.
- Expertise architecting AWS-based MLOps solutions using Amazon SageMaker, S3, AWS Step Functions, AWS CloudFormation, Amazon CloudWatch, Amazon MSK, and Amazon Bedrock.
- Deep experience building and deploying scalable NLP solutions, including multilingual data, sarcasm detection, and polysemy challenges.
- Experience with supervised and unsupervised learning, deep learning, LLMs, RAG, and prompt engineering.
- Proficiency with PyTorch, TensorFlow, and scikit-learn, including adapting and tuning open-source or pre-trained models.
- Strong software engineering knowledge covering design patterns, data structures, testing, security, version control, CI/CD, and regression testing.
- Experience applying model observability for issue detection and root-cause analysis.
- Ability to translate complex business problems into technical solutions and communicate findings to non-technical stakeholders.
- Demonstrated technical leadership and mentorship experience.
Benefits
- Hybrid work arrangement, indicated by the #LI-Hybrid designation.
- Equal employment opportunity and commitment to diversity and inclusion.
- Standard background verification is part of the selection process and is conducted with consent and limited to role-relevant information.
- Customer-focused, transparent, collaborative, and innovation-oriented company culture.
Categories