AI Model Engineer

3 days ago

Chennai, Tamil Nadu, India beBeeMachineLearningEngineer Full time ₹ 1,50,00,000 - ₹ 2,00,00,000
About the Role

MLOps Engineers are pivotal in deploying, scaling, and managing machine learning models in production environments.

They collaborate closely with data scientists and engineering teams to automate the ML lifecycle, optimize model performance, and ensure seamless integration with data pipelines.

Key Responsibilities
  • A bachelor's degree in computer science, analytics, mathematics, or statistics is required.
  • Strong experience in Python, SQL, Pyspark, and containerization technologies like Docker, Podman, and Kubernetes is essential.
  • Proficiency in CI/CD pipelines, model monitoring, and MLOps platforms such as AWS SageMaker, Azure ML, and MLFlow is a must.
  • Familiarity with cloud platforms, specifically AWS, Azure, and GCP, is highly valued.
  • Experience with ML frameworks like TensorFlow, PyTorch, and Scikit-learn is beneficial.
  • Familiarity with batch processing integration for large-scale data pipelines is preferred.
  • Experience with serving models using FastAPI, Flask, or similar frameworks for real-time inference is advantageous.
Qualifications and Skills
  • Bachelor's degree in computer science, analytics, mathematics, or statistics.
  • Required skills include:
    • Model Development & Tracking: TensorFlow, PyTorch, scikit-learn, MLflow, Weights & Biases.
    • Model Packaging & Serving: Docker, Kubernetes, FastAPI, Flask, ONNX, TorchScript.
    • CI/CD & Pipelines: GitHub Actions, GitLab CI, Jenkins, ZenML, Kubeflow Pipelines, Metaflow.
    • Infrastructure & Orchestration: Terraform, Ansible, Apache Airflow, Prefect.
    • Cloud & Deployment: AWS, GCP, Azure, Serverless (Lambda, Cloud Functions).
    • Monitoring & Logging: Prometheus, Grafana, ELK Stack, WhyLabs, Evidently AI, Arize.
    • Testing & Validation: Pytest, unittest, Pydantic, Great Expectations.
    • Feature Store & Data Handling: Feast, Tecton, Hopsworks, Pandas, Spark, Dask.
    • Message Brokers & Data Streams: Kafka, Redis Streams.
    • Vector DB & LLM Integrations (optional): Pinecone, FAISS, Weaviate, LangChain, LlamaIndex, PromptLayer.
    Prioritized Skills
    • Experience with Databricks is highly valued.
    • Certifications in AWS, Azure, or ML technologies are a plus.

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