
Chief AI Innovator
7 hours ago
Key Role: Visionary AI Leader
We are seeking a talented and experienced Lead Artificial Intelligence Engineer to spearhead the design, development, and deployment of advanced machine learning solutions.
In this strategic 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 position is ideal for someone who thrives at the intersection of innovation, engineering rigor, and business value.
Key Responsibilities:- Technical Strategy: Develop and implement the architecture, development, and deployment of machine learning models and AI systems across various 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 Delivery: 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 Governance: Establish standards for model quality, reproducibility, documentation, versioning, and monitoring.
- 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:
- Programming skills: Strong programming skills in Python and experience with ML libraries such as Scikit-learn, TensorFlow, PyTorch, Hugging Face.
- Machine Learning Fundamentals: Deep understanding of ML fundamentals: feature engineering, model evaluation, optimization, and deployment.
- Data Pipelines: Proficiency in designing and building data pipelines, real-time processing, and model inference systems.
- Cloud Platforms: Experience with cloud platforms (AWS, GCP, or Azure), containerization (Docker, Kubernetes), and CI/CD pipelines.
- MLOps Tools: Familiarity with MLOps tools (e.g., MLflow, DVC, Airflow, SageMaker) and vector databases (e.g., FAISS, Pinecone).
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