ML Ops Architect|Multiple|6+Yrs
- Aparajita Consultancy Services
- Multiple
- 6 - 10 Yrs
- 15 - 17 Lacs PA
Job Closed
Job Description
- As a ML Ops Architect, you will play a critical role in designing, implementing, and optimizing end-to-end machine learning systems.
- Your expertise will bridge the gap between data science, software engineering, and operations. Here are the key responsibilities and qualifications:
- 6+ years of experience as Architect in ML Ops, ML engineering, or related roles.
- Strong understanding of ML algorithms, deep learning, and NLP.
- Experience with vector databases (Faiss, Milvus) and similarity search.
- Proficiency in Python, PyTorch, and TensorFlow.
- Knowledge of cloud platforms (AWS, GCP, Azure) and containerization.
- Excellent problem-solving skills and ability to work in a fast-paced environment.
- Familiarity with other ML frameworks (e.g., scikit-learn, XGBoost).
- Experience with model explainability and interpretability techniques.
- Knowledge of ML model deployment using serverless architectures.
- Understanding of ML monitoring tools (e.g., Prometheus, Grafana).
Job Responsibilities
- Develop robust and scalable ML pipelines using tools like Kubeflow or custom solutions.
- Implement data version control (DVC) for tracking changes in datasets.
- Optimize data processing steps, feature engineering, and model training.
- Model Deployment and Monitoring:
- Deploy ML models to production environments (on-premises or cloud).
- Set up monitoring for model performance, data drift, and concept drift.
- Implement model-serving frameworks (e.g., TensorFlow Serving, TorchServe).
- Leverage vector databases (e.g., Faiss, Milvus) for efficient storage and retrieval of high-dimensional embeddings.
- Optimize similarity search for recommendation systems, content matching, and clustering.
- Proficiency in Python and PyTorch for model development and experimentation.
- Familiarity with TensorFlow for deep learning workflows.
- Write clean, maintainable, and efficient code.
- Design infrastructure for distributed ML training and inference.
- Optimize resource allocation (CPU, GPU, memory) for scalability.
- Work with containerization (Docker, Kubernetes) and orchestration tools.
- Implement GitOps practices for model versioning and deployment.
- Define CI/CD pipelines for automated testing, validation, and deployment.
- Ensure reproducibility and traceability.
- Mentor junior team members and guide them in best practices.
- Collaborate with data scientists, engineers, and DevOps teams.
- Stay updated with industry trends and emerging technologies.
Location
Bengaluru, Karnataka, India
Pune, Maharashtra, India