MLOps Architect

1 week ago

pune, 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 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).

(ref:hirist.tech)

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