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MLOps Architect
4 months ago
Role : ML Ops Architect
Experience : 10+ years ( Atleast 6+ years as Architect Role)
Location : Bangalore / Pune - Hybrid
Must to have skills :
- Data Drift
- Concept drift
- RAG
- Vector database
- Hiber parameter tuning
- ML concepts
- CI/CD Pipeline
ML Ops Architect JD :
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 :
System Design and Architecture :
- Collaborate with cross-functional teams to design ML systems that scale efficiently.
- Design ML pipelines, considering data ingestion, pre-processing, model training, and deployment.
- Evaluate and select appropriate vector databases (e.g., Faiss, Milvus) for efficient similarity search and retrieval
ML Pipeline Development :
- 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).
Vector Databases and Similarity Search :
- 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.
Coding and Scripting :
- Proficiency in Python and PyTorch for model development and experimentation.
- Familiarity with TensorFlow for deep learning workflows.
- Write clean, maintainable, and efficient code.
Infrastructure and Scalability :
- Design infrastructure for distributed ML training and inference.
- Optimize resource allocation (CPU, GPU, memory) for scalability.
- Work with containerization (Docker, Kubernetes) and orchestration tools.
GitOps and CI/CD :
- Implement GitOps practices for model versioning and deployment.
- Define CI/CD pipelines for automated testing, validation, and deployment.
- Ensure reproducibility and traceability.
Collaboration and Leadership :
- 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.
Qualifications : Bachelors or Master's degree in Computer Science, Data Science, or related field.
Must Have Skills :
- 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.
Good to Have Skills :
- 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).
