Senior AI Solutions Architect

12 hours ago

Vadodara, Gujarat, India beBeeArtificial Full time ₹ 1,50,00,000 - ₹ 2,00,00,000
Job Title">

Lead AI Engineer.

We are seeking a visionary and technically skilled professional to spearhead the design, development, and deployment of advanced machine learning solutions. This role is ideal for someone who thrives at the intersection of innovation, engineering rigor, and business value.

">Key Responsibilities:">
  • 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.
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Requirements:

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  • Education: Bachelor's or Master's in Computer Science, Machine Learning, Data Science, or related technical field.
  • Experience: 6-10+ years of experience in software engineering or AI/ML, with at least 2+ years in a technical leadership role.
  • Technical Expertise:
    • 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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