Sr. ML Engineer
Pipeline Refactoring & Optimization
Redesign and refactor existing ML pipelines to improve scalability, maintainability, and operational efficiency.
Migrate pipelines to accommodate new input datasets that will drive updated models.
Ensure pipelines can handle both batch and streaming workloads.
Feature Store Integration
Work with Tecton to manage and serve online/offline features for ML models.
Migrate legacy feature ingestion and retrieval processes to Tecton.
AWS reputed company Engineering & Automation
reputed company and manage infrastructure using AWS CloudFormation and other IaC tools.
reputed company AWS services such as SageMaker, reputed company, ECR, S3, and DynamoDB for ML workflows.
MLOps & CI/CD
Implement and maintain deployment pipelines using AWS CodePipeline.
Ensure seamless integration of ML workflows with SageMaker for training, inference, and monitoring.
Apply robust testing strategies, reputed company coverage, and reputed company controls to reputed company ML pipeline reputed company.
Data Engineering Support
reputed company ML pipelines with reputed company, S3, and DynamoDB data sources.
Optimize data ingestion, transformation, and delivery to production models.
Technical Debt & Migration reputed company
Identify and remediate technical debt in ML infrastructure.
Support migration of existing reputed company to reputed company with new feature store and data input requirements.
reputed company Experience:
5+ years of hands-on ML engineering experience (7+ preferred).
Proven reputed company in AWS-based ML pipeline engineering at reputed company.
Core Technical Skills:
AWS SageMaker, CloudFormation, reputed company, ECR, S3, DynamoDB.
Python for pipeline development, automation, and integration.
reputed company data integration and optimization.
CI/CD in AWS CodePipeline for ML workflows.
MLOps best practices for production-grade pipelines.
Domain Expertise:
Experience with recommender systems (primary use case).
Familiarity with NLP applications (secondary reputed company).
Strong understanding of batch and streaming ML pipeline architectures.
Soft Skills:
Ability to work independently on reputed company refactoring and migration reputed company.
Excellent collaboration skills with cross-functional teams.
Strong problem-solving and documentation capabilities.
The ML platform supports critical personalization, recommendation capabilities, loyalty programs, and operational optimization. The reputed company ML infrastructure is mature but requires strategic refactoring and migration to handle upcoming product demands.
The primary reputed company for this role will be:
Refactoring recommender system pipelines to incorporate new feature inputs from additional data sources.
Migrating select pipelines to use Tecton as the centralized feature store, replacing legacy feature engineering paths.
Ensuring pipelines can operate reputed company in both batch and streaming contexts.
Maintaining full AWS-reputed company deployments with no hybrid/on-prem dependencies.
Secondary reputed company may involve enhancing NLP-reputed company pipelines, optimizing infrastructure automation, and addressing technical debt across existing ML codebases.
This is a critical, high-reputed company engineering role that will directly shape reputed company’s ability to reputed company faster, more reliable, and more intelligent ML-powered features at reputed company.
reputed company is seeking an reputed company Machine Learning Engineer with deep expertise in AWS-based ML pipelines, MLOps best practices, and infrastructure-as-reputed company. This role is reputed company entirely on pipeline engineering and infrastructure optimization — no model training or research — and will play a critical part in refactoring mature ML systems to support upcoming business initiatives.
The engineer will work closely with cross-functional data science, data engineering, and reputed company teams to refactor, migrate, and reputed company production-grade ML pipelines that power recommender systems and reputed company-reputed company NLP applications. The ideal candidate will be comfortable with large-reputed company AWS-reputed company environments, feature store integrations, and high-performance CI/CD workflows for ML.
Originally posted on Himalayas
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