Job Title: Senior Machine Learning Engineer 57 Years Experience)
Location: Bangalore/Gurgaon
Job Summary:
Senior ML Engineer with 57 years of strong experience in building deploying and managing
AI/ML models in enterprise grade production environment. This role involves collaborating with
data scientists to develop models integrating them into scalable MLOps pipelines and
optimizing endtoend ML deployment workflows. The ideal candidate will design reusable ML
Ops frameworks for model training inference and lifecycle management.
Key Responsibilities:
Collaborate with Data Scientists to develop optimize and finetune ML models.
Collaborate with application developers to understand the business use case flows and
interfaces to create implement integrate and deploy endtoend MLOps pipelines.
Identify create and implement MLOps pipeline for model training versioning validation
and deployment.
Automate model training evaluation monitoring and retraining workflows.
Implement feature stores model registries and scalable inference architectures.
Deploy models as microservices batch jobs or realtime APIs for low latency high
throughput and resource efficiency.
Ensure NFR requirements of the MLOps pipeline such as availability reliability
scalability and security.
Ensure model explainability monitoring drift detection and governance.
Work with CI/CD pipelines Kubernetes and containerized ML workflows for scalable
deployments.
Required Skills & Experience:
57 years of handson experience in ML Model Integration: TensorFlow PyTorch ONNX
etc.
Strong experience in MLOps & Model Deployment: Kubeflow MLflow Airflow Prefect
etc
Experience with Feature Stores & Model Registry
Expertise in Big Data & Streaming pipelines: Apache Kafka Spark Flink ClickHouse
Strong exposure in infrastructure & deployment of Docker Kubernetes Terraform and
cloud (AWS/GCP/Azure) based ML services
Experience with model performance optimization techniques such as model
quantization pruning distillation GPU acceleration would be an added advantage
Experience in model monitoring & governance such model drift detection explainability
(SHAP LIME) logging & observability (Prometheus Grafana)