AI Summary
We are seeking a Senior Machine Learning Engineer to design and implement end-to-end ML solutions. The ideal candidate will have experience with ML frameworks, deep learning, and cloud platforms. The role involves technical delivery, collaboration, and innovation.
Key Highlights
Design and implement end-to-end ML solutions
Collaborate with cross-functional teams
Stay current with ML research and emerging technologies
Key Responsibilities
Design and implement end-to-end ML solutions from experimentation to production
Build scalable ML pipelines and infrastructure
Optimize model performance, efficiency, and reliability
Technical Skills Required
Benefits & Perks
Long-term B2B collaboration
Fully remote setup
Budget for medical insurance
Paid sick leave, vacation, public holidays
Nice to Have
Practical experience with cloud platforms
Practical experience with deep learning models
Experience with taxonomies or ontologies
Job Description
Responsibilities:
- Technical Delivery (60%)
- Design and implement end-to-end ML solutions from experimentation to production;
- Build scalable ML pipelines and infrastructure;
- Optimize model performance, efficiency, and reliability;
- Write clean, maintainable, production-quality code;
- Conduct rigorous experimentation and model evaluation;
- Troubleshoot and resolve complex technical challenges
- Collaboration and Contribution (25%);
- Mentor junior and mid-level ML engineers;
- Conduct code reviews and provide constructive feedback;
- Share knowledge through documentation, presentations, and workshops;
- Collaborate with cross-functional teams (DevOps, Data Engineering, SAs);
- Contribute to internal ML practice development
- Innovation and Growth (15%)
- Stay current with ML research and emerging technologies;
- Propose improvements to existing solutions and processes;
- Contribute to the development of reusable ML accelerators;
- Participate in technical discussions and architectural decisions
- Machine Learning Core
- ML Fundamentals: supervised, unsupervised, and reinforcement learning;
- Model Development: feature engineering, model training, evaluation, hyperparameter tuning, and validation;
- ML Frameworks: classical ML libraries, TensorFlow, PyTorch, or similar frameworks;
- Deep Learning: CNNs, RNNs, Transformers
- LLMs and Generative AI
- LLM Applications: Experience building production LLM-based applications;
- Prompt Engineering: Ability to design effective prompts and chain-of-thought strategies;
- RAG Systems: Experience building retrieval-augmented generation architectures;
- Vector Databases: Familiarity with embedding models and vector search;
- LLM Evaluation: Experience with evaluation metrics and techniques for LLM outputs
- Data and Programming
- Python: Advanced proficiency in Python for ML applications;
- Data Manipulation: Expert with pandas, numpy, and data processing libraries;
- SQL: Ability to work with structured data and databases;
- Data Pipelines: Experience building ETL/ELT pipelines - Big Data: Experience with Spark or similar distributed computing frameworks
- MLOps and Production
- Model Deployment: Experience deploying ML models to production environments;
- Containerization: Proficiency with Docker and container orchestration;
- CI/CD: Understanding of continuous integration and deployment for ML;
- Monitoring: Experience with model monitoring and observability;
- Experiment Tracking: Familiarity with MLflow, Weights and Biases, or similar tools
- Cloud and Infrastructure
- AWS Services: Strong experience with AWS ML services (SageMaker, Lambda, etc.);
- GCP Expertise: Advanced knowledge of GCP ML and data services;
- Cloud Architecture: Understanding of cloud-native ML architectures;
- - Infrastructure as Code: Experience with Terraform, CloudFormation, or similar
Interested in remote work opportunities in Devops? Discover Devops Remote Jobs featuring exclusive positions from top companies that offer flexible work arrangements.
Browse our curated collection of remote jobs across all categories and industries, featuring positions from top companies worldwide.
- Practical experience with cloud platforms (AWS stack is preferred, e.g. Amazon SageMaker, ECR, EMR, S3, AWS Lambda);
- Practical experience with deep learning models;
- Experience with taxonomies or ontologies;
- Practical experience with machine learning pipelines to orchestrate complicated workflows;
- Practical experience with Spark/Dask, Great Expectations
- Long-term B2B collaboration;
- Fully remote setup;
- A budget for your medical insurance;
- Paid sick leave, vacation, public holidays;
- Continuous learning support, including unlimited AWS certification sponsorship
- Recruitment Interview;
- Tech interview;
- HR Interview;
- HM Interview
Similar Jobs
Explore other opportunities that match your interests
Visa Sponsorship
Relocation
Remote
Job Type
Full-time
Experience Level
Not Applicable
CivicActions
United State
Visa Sponsorship
Relocation
Remote
Job Type
Full-time
Experience Level
Not Applicable
Jobgether
India
Senior Cloud Platform Engineer - Control Plane Infrastructure
••••••
••••••
••••••
Job Type
••••••
Experience Level
••••••
ClickHouse
United State