MLOps Architect

1 week ago

Pune, Maharashtra, India Aparajita Consultancy services Full time

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 crossfunctional teams to design ML systems that scale efficiently.
  • Design ML pipelines, considering data ingestion, preprocessing, 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 (onpremises or cloud).
  • Set up monitoring for model performance, data drift, and concept drift.
  • Implement modelserving frameworks (e.g., TensorFlow Serving,TorchServe).
Vector Databases and Similarity Search :

  • Leverage vector databases (e.g., Faiss, Milvus) for efficient storage and retrieval of highdimensional 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 problemsolving skills and ability to work in a fastpaced environment.

Good to Have Skills :

  • Familiarity with other ML frameworks (e.g., scikitlearn, 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).
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