AI Technical Lead

Overview

We are looking for a highly skilled and hands-on AI Technical Lead to drive the technical execution of our AI/ML initiatives. This role combines deep technical expertise in AI/ML with leadership responsibilities, including solution architecture, code and model reviews, mentoring developers and data scientists, and ensuring best practices in model development, deployment, and maintenance.

Responsibilities

Key Responsibilities:

• Technical Leadership:Lead the design, development, and deployment of AI/ML models and pipelines. Define architecture and best practices for scalable, production-ready AI systems.
• Hands-On Development:Write, review, and optimize Python code, ML models, and data processing pipelines. Ensure robustness, efficiency, and reproducibility.
• Model Lifecycle Management:Manage end-to-end ML model lifecycle including data preprocessing, feature engineering, model training, validation, tuning, deployment, and monitoring (MLOps).
• Team Collaboration & Mentoring:Provide technical guidance and mentorship to data scientists, ML engineers, and junior developers. Facilitate code reviews and knowledge sharing.
• Tooling & Infrastructure:Select and implement appropriate tools, frameworks, and platforms (e.g., TensorFlow, PyTorch, MLflow, Kubeflow, etc.) to accelerate AI development.
• DevOps & MLOps Integration:Work closely with DevOps teams to operationalize models using CI/CD pipelines and cloud-native infrastructure (AWS/GCP/Azure).
• Cross-Functional Engagement:Collaborate with product managers, architects, and business stakeholders to understand requirements, estimate effort, and align technical decisions with product goals.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or related field.

Essential skills

• 6+ years of experience in software engineering or ML development, with at least 2+ years in a technical leadership role.
• Strong coding expertise in Python, and experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Experience deploying models to production environments using MLOps best practices.
• Solid understanding of data structures, algorithms, and software design principles.
• Proficiency with cloud platforms (AWS, Azure, or GCP) and containerization tools (Docker, Kubernetes).
• Excellent debugging, optimization, and performance tuning skills.

• Experience with LLMs, NLP, or computer vision projects.
• Familiarity with data lake architectures, feature stores, or real-time inference.
• Contributions to open-source AI/ML projects or publications in the field.