ML-Ops
Sia · Mumbai, Maharashtra, India
Apply directly on Sia’s careers site — no account needed.
About the role
We’re looking for a hands-on Data Scientist with strong Python and backend skills to maintain and improve production algorithms. The focus is on performance optimization, parallel processing, bug fixing, and building/maintaining REST APIs (Django/FastAPI). ML/DL is applied pragmatically; GCP is a plus.
Key Responsibilities
- Maintain and improve production algorithms; triage and fix bugs; add regression tests
- Profile and optimize code (CPU/memory/I/O); apply parallelism/concurrency where relevant
- Design and maintain REST APIs (Django/DRF or FastAPI) for model/algorithm services
- Test for efficiency and scalability; benchmark latency/throughput; right-size costs
- Ensure high code quality: unit/integration tests, type hints, CI/CD, documentation
- Collaborate with product/data/QA to prioritize improvements and meet SLAs
- 5–7 years in data science/software roles with production-grade Python
- Strong Python expertise (pandas, NumPy), profiling tools (cProfile, line_profiler, memory_profiler, py-spy)
- Parallel/concurrent processing (multiprocessing, threading, asyncio) and performance tuning
- REST API development with Django/DRF or FastAPI (auth, error handling, pagination)
- ML fundamentals with scikit-learn; experience deploying models to services
- Testing/quality: pytest/unit test, CI/CD, Git workflows; clear communication and documentation
What We Offer
Opportunity to lead cutting-edge AI projects in a global consulting environment.
Leadership development programs and training sessions at our global centers.
A dynamic and collaborative team environment with diverse projects.
Position based in Mumbai (onsite)
Sia is an equal opportunity employer. All aspects of employment, including hiring, promotion, remuneration, or discipline, are based solely on performance, competence, conduct, or business needs.
Skills
- Python
- REST
- Django
- FastAPI
- pandas
- NumPy
- GitHub Actions
- scikit-learn
- SQL
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