Job Description : Senior Data Scientist & Machine Learning Engineer

Role Overview :

We are seeking a highly experienced, versatile Senior Data Scientist & Machine Learning Engineer to bridge the gap between advanced data science, full-stack software engineering, and production-grade ML operations (MLOps). In this role, you will design, build, and deploy scalable machine learning models and full-stack applications. You will collaborate closely with business stakeholders to translate complex technical capabilities into strategic business value, while leveraging cutting-edge AI-assisted development workflows to accelerate delivery.

Key Responsibilities :

- 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 team's 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.

Required Technical Skills & Qualifications :

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 :

1. Python (for data science, modeling, and scripting)

2. JavaScript / TypeScript (for full-stack integration and modern web frameworks)

3. SQL (for complex data querying and relational database management)

4. Shell Scripting (for system automation and environment configuration)

Cloud, DevOps & Infrastructure :

- 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 Engineering & Governance :

- 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.

Next-Generation AI & Vibe-Coding Practices :

- 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.

Soft Skills & Professional Attributes :

- 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.