Lead Machine Learning Architect

Jobgether; Remote; IDR 2.939.200.000 - 3.359.087.000; Full time

Job summary

A leading tech recruitment platform is seeking a remote Machine Learning Architect to design and optimize ML solutions on AWS. The ideal candidate will have over 5 years of IT experience and extensive AWS expertise. Responsibilities include leading ML architecture design, managing data workflows, and mentoring junior team members. This role offers a competitive compensation ranging from $175K to $200K. Join a collaborative team culture focused on innovation and professional development.

Benefits

Competitive OTE Compensation

Flexible remote work environment

Opportunities for professional growth

Qualifications

• 5+ years of professional IT or software engineering experience.
• 2+ years of hands-on AWS experience in ML and data workloads.
• Strong knowledge of AWS data services like Amazon S3, AWS Glue, and Amazon RDS.

Responsibilities

• Design, implement, and maintain end-to-end ML architecture using AWS services.
• Lead data acquisition, cleaning, and transformation workflows.
• Deploy and manage models using SageMaker endpoints and pipelines.

Skills

AWS services

Machine Learning

Python

SageMaker

Data Governance

SQL

Education

AWS Certification

Tools

SageMaker Studio

Scikit-Learn

TensorFlow

PyTorch

Pandas

Job description

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Machine Learning Architect - REMOTE. In this role, you will be pivotal in designing, deploying, and optimizing data-driven machine learning solutions on AWS. Your expertise will drive the creation of secure and scalable ML systems, enabling effective data management and model deployment. You'll engage closely with clients and lead the enhancement of best practices within the data and ML lifecycle, making a substantial impact across projects and teams. Your guidance will not only support client objectives but also foster growth among team members.

Accountabilities

• Designing, implementing, and maintaining end-to-end ML architecture using AWS services
• Leading data acquisition, cleaning, and transformation workflows using AWS Glue and Lambda
• Building scalable data pipelines to feed ML models with high-quality, production-grade data
• Collaborating with data engineers and scientists to optimize model input/output processes
• Deploying and managing models using SageMaker endpoints, pipelines, and the model registry
• Selecting appropriate AWS storage and database services (S3, RDS, Redshift) to support ML use cases
• Developing automated workflows for model training, evaluation, and retraining using Step Functions and MLOps best practices
• Assisting clients in migrating legacy ML solutions to cloud-native platforms
• Troubleshooting data pipeline and model deployment issues
• Participating in project planning, client meetings, and delivery reviews
• Contributing to internal R&D projects that evaluate new AWS ML and data services
• Mentoring junior team members on data modeling, ML deployment, and data architecture best practices
• Remaining up to date with ML, AI, and data technology trends
• Advising clients on responsible AI practices, data governance, and compliance in model development

Requirements

• 5+ years of professional IT or software engineering experience
• 2+ years of hands‑on AWS experience in ML and data workloads
• At least one AWS Certification (preferably Machine Learning – Specialty or Solutions Architect – Professional)
• Experience with Amazon SageMaker: model training, hosting, custom containers, and Pipelines
• Proficiency with SageMaker Studio for end‑to‑end ML development
• Strong knowledge of AWS data services including Amazon S3, AWS Glue, and Amazon RDS
• Familiarity with streaming data and batch processing using Lambda, Step Functions, or Kafka
• Proficiency in Python and frameworks such as Scikit‑Learn, TensorFlow, PyTorch, and Pandas
• Experience with NLP and CV services like Amazon Comprehend and Rekognition
• Strong SQL skills and familiarity with both relational and NoSQL data stores
• Knowledge of data modeling, dimensional modeling, and building feature stores
• Experience designing and implementing MLOps workflows, CI/CD, and monitoring practices
• Understanding of data privacy, model drift, bias detection, and explainability techniques
• Bonus: Experience working with big data platforms like Apache Spark, EMR, or Lake Formation

Benefits

• Competitive OTE Compensation Range: $175K-$200K dependent on experience
• Flexible remote work environment with minimal travel required
• Opportunities for professional growth and development
• Access to cutting‑edge technologies and platforms
• Collaborative and supportive team culture
• Engagement in innovative and impactful projects
• Mentorship opportunities to develop leadership skills

Why Apply Through Jobgether?

We use an AI‑powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top‑fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre‑contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.