Senior AI Development Manager

2 weeks ago

Pushkar, Rajasthan, India beBeeArtificialintelligence Full time US$ 1,80,000 - US$ 2,50,000
Job Description

We are seeking a visionary and technically skilled AI development manager to spearhead the design, development, and deployment of advanced machine learning solutions.

  • Technical Leadership: Lead the architecture, development, and deployment of machine learning models and AI systems across a range of use cases.
  • Model Development: Design, train, and optimize supervised, unsupervised, and deep learning models using frameworks like PyTorch, TensorFlow, and XGBoost.
  • Mentorship: Coach and mentor a team of ML engineers and data scientists; foster a culture of innovation, ownership, and continuous learning.
  • Project Management: Drive planning, execution, and delivery of AI/ML projects, ensuring alignment with business objectives and technical feasibility.
  • System Design: Architect scalable, secure, and high-performance ML pipelines and services using cloud-native tools and MLOps best practices.
  • Collaboration: Work closely with product managers, data engineers, and DevOps teams to translate business problems into AI-driven solutions.
  • Code Quality & Governance: Establish standards for model quality, reproducibility, documentation, versioning, and monitoring.
  • Innovation: Stay current with research and industry trends in ML/AI, evaluate new tools, and introduce state-of-the-art solutions where applicable.
Required Skills and Qualifications
  • Bachelor's or Master's in Computer Science, Machine Learning, Data Science, or related technical field.
  • 6–10+ years of experience in software engineering or AI/ML, with at least 2+ years in a technical leadership role.
  • Strong programming skills in Python and experience with ML libraries such as Scikit-learn, TensorFlow, PyTorch, Hugging Face.
  • Deep understanding of ML fundamentals: feature engineering, model evaluation, optimization, and deployment.
  • Proficiency in designing and building data pipelines, real-time processing, and model inference systems.
  • Experience with cloud platforms (AWS, GCP, or Azure), containerization (Docker, Kubernetes), and CI/CD pipelines.
  • Familiarity with MLOps tools (e.g., MLflow, DVC, Airflow, SageMaker) and vector databases (e.g., FAISS, Pinecone).

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