About this role
Accountabilities
- Design and build distributed data pipelines capable of processing large and complex datasets.
- Develop, deploy, and operate machine learning models in production environments.
- Build agent-based AI systems capable of interacting with external services and internal infrastructure.
- Design scalable architectures for data processing, ML training, deployment, and inference pipelines.
- Establish and maintain observability across pipelines, models, and AI agents, including metrics, tracing, and alerting.
- Evaluate different modeling approaches and optimize trade-offs between cost, performance, reliability, and scalability.
- Collaborate with product and customer teams to develop solutions that deliver measurable business impact.
- Take ownership of projects end-to-end, moving rapidly from prototypes and experimentation to production-ready systems.
- Contribute to reliable, well-documented, and maintainable software architectures.
Requirements
- Strong professional experience developing and operating production machine learning systems.
- Solid experience with Apache Spark and SQL in distributed data-processing environments.
- Proven experience working with large, complex datasets and designing scalable data-processing solutions.
- Experience building training, deployment, and monitoring pipelines for machine learning models.
- Experience working with cloud services across data, compute, and machine learning workloads, particularly AWS .
- Strong programming skills in Python and Scala .
- Experience with AWS SageMaker, AWS Bedrock, and Kubernetes is highly relevant.
- Ability to design clear software architectures and produce well-documented technical systems.
- Strong technical communication, collaboration, and problem-solving skills.
- Ability to take strong technical ownership and work effectively across the complete lifecycle of ML systems.
- Experience taking products or technical solutions from 0 to production , particularly in startup or high-growth environments, is a plus.
- Experience with large geospatial datasets and indexing strategies is desirable.
- Experience building AI agents capable of operating at scale is a plus.
- Knowledge of LLM fine-tuning, distillation, or self-hosting is desirable.
- Background in traditional machine learning, particularly with messy datasets and rigorous evaluation methodologies, is a plus.
- Experience with CI/CD, containerization, and infrastructure as code is desirable.
Benefits
- Contractor agreement.
- Compensation paid in USD .
- Fully remote work.
- Opportunity to work on advanced AI and machine learning infrastructure.
- Highly technical environment with significant engineering ownership.
- Opportunity to design and scale systems that move rapidly from concept to production.
- Exposure to distributed systems, machine learning, AI agents, cloud infrastructure, and large-scale data engineering.
How Jobgether works:
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!
Why Apply Through Jobgether?
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.
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