TI
AI/ML Ops
Tranzeal Inc.
- 🇮🇳 India
- Hybrid
- 9 months ago
- MLOps
- Docker
- SAS
- SPSS
- Python
- Airflow
- Kubeflow
- Kubernetes
- FastAPI
- Flask
- Canary Releases
- ONNX
- scikit-learn
- XGBoost
- Prometheus
- Grafana
- Dagster
9 months ago
Location: Bellandur, Bengaluru, Karnataka – 560103 (Hybrid work model)
Role overview
Experienced MLOps Engineer with 5–7 years experience to modernize legacy ML models through containerization, retraining pipelines, and production monitoring.
Main focus: Migrate existing monolithic/statistical models to Docker containers, establish automated retraining/monitoring, and enhance model performance while maintaining business continuity.
Key responsibilities
- Containerize legacy ML models (SAS, R, SPSS, Python notebooks, custom binaries) using Docker for standardized deployment and scalability.
- Reverse-engineer legacy model logic from production codebases, spreadsheets, or vendor black-boxes to create reproducible training pipelines.
- Build automated retraining pipelines for legacy models using modern orchestration (Airflow/Kubeflow) with data versioning and validation gates.
- Deploy legacy models to Kubernetes alongside new ML models, implementing API wrappers (FastAPI/Flask) for unified model serving.
- Set up comprehensive monitoring for legacy models tracking inference latency, prediction drift, data quality degradation, and business KPI impact.
- Gradually replace/improve legacy models through A/B testing, shadow deployments, and canary releases while maintaining 99.9% uptime.
- Document legacy model assumptions, limitations, and migration roadmaps for audit and stakeholder review.
- Manage model registry containing both legacy and modern models with versioning, lineage tracking, and approval workflows.
Required skills and experience
- 5–7 years MLOps/ML Engineering experience, including hands-on legacy model migration projects.
- Proven Docker expertise for containerizing diverse ML artifacts (pickles, ONNX, PMML, custom executables).
- Experience debugging and replicating legacy models from production logs, spreadsheets, or vendor documentation.
- Kubernetes deployment experience with hybrid model serving (legacy + modern ML).
- Strong Python + experience with legacy ML libraries (scikit-learn older versions, XGBoost, statsmodels, PMML).
- Monitoring stack implementation (Prometheus/Grafana) for both statistical and ML models.
Preferred qualifications
- Experience with SAS, R, SPSS model migration to Python/container ecosystems.
- Model governance frameworks for regulated industries with legacy model inventory management.
- Airflow/Dagster for orchestrating legacy retraining schedules tied to business events.
AI/ML Ops · Tranzeal Inc.