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:





- 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)

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: Solid 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.

📌 Senior Data Scientist & Machine Learning Engineer (Bengaluru)
🏢 Trigent Software
📍 Bengaluru