Expert Machine Learning Solutions Architect

1 week ago

Bengaluru, Karnataka, India beBeeMachineLearning Full time US$ 1,20,000 - US$ 2,00,000
Job Description

We are seeking a highly skilled and experienced Senior AI/ML Engineer to join our growing team. This role is ideal for someone who has deep expertise in machine learning model development and deployment, MLOps practices, and a strong grasp of modern tools and frameworks used for scalable, production-grade machine learning solutions.

Responsibilities
  • Design, build, and optimize machine learning models using standard algorithms (e.g., classification, regression, clustering).
  • Work with Large Language Models (LLMs) and have foundational understanding of Generative AI concepts and applications.
  • Deploy models to production environments with robust monitoring, alerting, and logging.
  • Implement MLOps best practices, ensuring reproducibility, scalability, and continuous improvement of ML workflows.
  • Develop and manage CI/CD pipelines for model training, evaluation, and deployment.
  • Build scalable API services using Flask or FastAPI to expose ML models.
  • Containerize applications and services using Docker.
  • Work with cloud platforms such as Azure or GCP for data storage, compute, and model deployment.
  • Collaborate with cross-functional teams including data scientists, product managers, and DevOps engineers.

Qualifications
  • Strong foundation in machine learning concepts and hands-on experience in model building and evaluation.
  • Experience with model inference pipelines and deploying models into real-world applications.
  • Proficient in Python and ML libraries like scikit-learn, XGBoost, TensorFlow, PyTorch, etc.
  • Hands-on experience with CI/CD tools like GitHub Actions, Jenkins, Azure DevOps, or GitLab CI.
  • Solid understanding of MLOps workflows, tools like MLflow, Kubeflow, or TFX.
  • Experience developing APIs using Flask/FastAPI and deploying them using Docker.
  • Familiarity with cloud environments (Azure/GCP) and associated ML services.
  • Experience monitoring deployed models (e.g., drift detection, performance tracking).

Nice to Have:


  • Experience with feature stores, vector databases, or streaming data processing.
  • Exposure to LLM fine-tuning, prompt engineering, or using models via APIs (e.g., OpenAI, Hugging Face).
  • Familiarity with data versioning and experiment tracking tools.

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