Still Struggling with Tech Hiring? Discover Olibr's Solution Now!

ML Ops Architect|Multiple|6+Yrs

  • Aparajita Consultancy Services
  • Multiple
  • 6 - 10 Yrs
  • 15 - 17 Lacs PA

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.


Bengaluru, Karnataka, India

Pune, Maharashtra, India