| Job Description: |
Skills: Digital : Machine Learning~Digital : Natural Language Processing (NLP)~AI Agents~AI & Gen AI - Products & Tools
Experience Required: 4-6
Role Summary
Builds, trains and tunes machine learning models. Translates data science experiments into scalable, production-ready ML solutions.
Key Responsibilities –
Translate data science prototypes into production-grade ML services and pipelines.
- Build training and inference code with reproducibility, versioning, and automated testing.
- Implement scalable model serving (online/offline), batching, and latency/throughput optimization.
- Integrate model lifecycle tooling (tracking, registry, deployment automation, monitoring).
- Collaborate with Data Engineering on feature pipelines and data contracts.
- Own production health: drift detection, performance regression, rollback strategies, and incident response.
Required Qualifications
- 5+ years software engineering with 2+ years shipping ML models to production.
- Strong Python skills and experience with ML frameworks (TensorFlow/PyTorch).
- Experience with containers and orchestration (Docker/Kubernetes) and API development.
- Understanding of ML system design (data leakage, training-serving skew, drift).
- CI/CD and DevOps practices applied to ML workloads (MLOps).
Preferred / Nice to Have
- Experience with feature stores, model registries, and model monitoring stacks.
- GPU optimization and distributed training experience.
- Experience with responsible AI toolkits and compliance requirements.
Core Skills (from POD sheet)
Python, TensorFlow, PyTorch, Docker, REST APIs
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