Role summary:

You will own the end-to-end delivery of ML- and data-driven capabilities for clientsturning business needs into production-grade data pipelines, deployed models (recommendation / NLP / GenAI), and measurable outcomes. This is a builder + owner role: you will be accountable for technical delivery, operational reliability, and proactive stakeholder expectation management.

Total Experience: 8-9 years

Core responsibilities:

- Own the end-to-end delivery of production-grade ML systems for recommendations, NLP, and personalization.

- Take full ownership of scalable data pipelines. Architect, build, and operate robust ETL/ELT systems.

- Establish and enforce data architecture standards. Design scalable storage patterns for analytics and ML.

- Drive MLOps as a discipline. Build and institutionalize CI/CD-aligned ML workflows.

- Deliver production-ready systems. Expose model and data capabilities through versioned APIs.

- Own stakeholder outcomes and client success.

Must-have Experience:

- End-to-end ownership of at least one production ML deliverable.

- Hands-on delivery of scalable data pipelines.

- Production software practices: testing, CI/CD, API design, monitoring.

- Client-facing delivery and stakeholder management.

- Practical data-lake experience.

Tech Stack & Qualifications:

- Bachelors degree in computer science, Data Analytics, Engineering, or a related discipline.

- Strong grasp of core data engineering principles, including data lake architectures, columnar storage formats, ETL/ELT frameworks, and BI ecosystems.

- Ability to operate effectively in independent and collaborative environments.

- Strong written and verbal communication skills.

- Sharp critical thinking and structured problem-solving abilities.

- High degree of ownership and adaptability.