Details for Senior Data Engineer Role Skills highlighted in yellow are must have skills.

Skills highlighted in blue are good to have skills.

: Senior Data Engineer &

- Machine Learning Engineer Role Overview We are seeking a highly experienced, versatile Data Engineer &
- 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: 4-8 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 up-to-date web frameworks) ▪ SQL (for complex data querying and relational database management) ▪ 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.

📌 Sr Data Engineer & ML Engineer - Deloitte (Bengaluru)
🏢 Trigent Software
📍 Bengaluru