Chief AI Strategist

1 week ago

Lucknow, Uttar Pradesh, India beBeeMachineLearning Full time ₹ 2,00,00,000 - ₹ 2,50,00,000
Lead AI/ML Engineer Job Opportunity\

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

In this leadership role, you will guide a team of AI/ML engineers, contribute hands-on to critical technical challenges, and collaborate with cross-functional teams to deliver impactful AI products.

This role is ideal for someone who thrives at the intersection of innovation, engineering rigor, and business value.

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  • 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.\
    • \
    • 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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