Senior Data Scientist & Machine Learning Engineer
Job Location:
Monthly Salary:
Posted on:
2 days ago
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Vacancies:
1 Vacancy
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Job Summary
Key Responsibilities
Core Software Engineering & Architecture
- System Architecture & Development: Architect develop and maintain robust full-stack applications and machine learning pipelines ensuring high availability scalability and performance.
- MLOps & CI/CD Pipeline Orchestration: Design and manage end-to-end Software Development Life Cycle (SDLC) pipelines implementing Continuous Integration Continuous Delivery and Continuous Training (CI/CD/CT) workflows.
- Stakeholder Collaboration: Partner directly with business clients and non-technical stakeholders to understand requirements present technical roadmaps and explain complex AI/ML features in an accessible impact-driven manner.
- Code Governance & Mentorship: Lead code reviews provide constructive feedback on merge requests and champion engineering best practices across the team.
- Data Engineering & Architecture: Design and optimize data ingestion transformation and persistence layers selecting the appropriate storage paradigms (SQL vs. NoSQL) and search technologies to support real-time and batch processing.
- AI-Assisted Engineering: Pioneer the integration of AI-assisted coding tools and vibe-coding methodologies into the teams workflow establishing guardrails context engineering standards and validation frameworks to ensure code safety and reliability.
- Data Governance & Security: Implement strict data protection data minimization and classification standards to ensure compliance with global regulatory frameworks.
Core Software Engineering & Architecture
- Experience: 10 15 years of full-stack software engineering experience in an enterprise or production environment.
- Design Patterns: Deep understanding of object-oriented and functional design patterns with a proven ability to write clean modular and testable code.
- Polyglot Programming: Proficiency in a diverse set of programming languages:
- Python (for data science modeling and scripting)
- Java (for enterprise-grade backend services)
- JavaScript / TypeScript (for full-stack integration and modern web frameworks)
- SQL (for complex data querying and relational database management)
- Shell Scripting (for system automation and environment configuration)
- AWS Ecosystem: Extensive experience deploying configuring monitoring and troubleshooting full-stack applications and ML workloads in Amazon Web Services (AWS) (e.g. SageMaker ECS/EKS Lambda S3 and IAM).
- CI/CD Platforms: Hands-on experience orchestrating automated build test and deployment pipelines using the GitLab platform.
- Data Management: Strong grasp of modern data ingestion ETL/ELT pipelines and data persistence strategies including relational (SQL) non-relational (NoSQL) and search indexing technologies.
- Data Security: Solid understanding of best practices for data protection data minimization and data classification.
- AI-Assisted Tooling: Practical experience utilizing AI-assisted coding platforms (e.g. Cursor GitHub Copilot Windsurf) to optimize development velocity.
- Vibe-Coding & Context Engineering: Advanced understanding of AI-driven development paradigms (vibe-coding) including context window management prompt structuring iterative refinement and the implementation of strict validation guardrails to prevent silent code failures or security vulnerabilities.
- Technical Translation: Exceptional communication skills with a proven track record of presenting technical topics model metrics and architectural decisions to non-technical business clients.
- Collaborative Leadership: Strong peer-review skills with a focus on fostering a collaborative high-quality engineering culture through constructive feedback on merge requests.
