Job Title: AI/ML Technical Lead
Role Summary
The AI/ML Technical Lead is responsible for leading the design, development, and deployment of machine learning and AI-driven solutions. This role involves overseeing a team of data scientists, ML engineers, and software developers, ensuring technical excellence, scalability, and business impact. The Technical Lead bridges research, engineering, and product teams to deliver end-to-end intelligent systems that solve complex business problems.
Key Responsibilities
1. Technical Leadership
- Lead the end-to-end lifecycle of AI/ML projectsfrom problem definition and data exploration to model deployment and monitoring.
- Architect and design scalable machine learning pipelines and production-ready systems.
- Evaluate and select appropriate ML frameworks, libraries, and infrastructure (e.g., TensorFlow, PyTorch, AWS Sagemaker, Azure ML, GCP Vertex AI).
- Review code, model performance, and solution design to maintain high-quality standards.
- Drive best practices in model versioning, experiment tracking, and CI/CD for ML (MLOps).
2. Team Leadership & Collaboration
- Mentor and guide data scientists, ML engineers, and junior developers.
- Foster a collaborative, innovative environment focused on continuous learning and technical excellence.
- Collaborate cross-functionally with product managers, data engineers, and business stakeholders to define and prioritize AI initiatives.
- Translate business objectives into AI/ML technical solutions and actionable deliverables.
3. Research & Innovation
- Stay current with emerging AI trends, tools, and methodologies (e.g., GenAI, LLMs, multimodal models, reinforcement learning).
- Prototype new techniques to improve performance, interpretability, and scalability of models.
- Evaluate academic research and transform it into practical, production-grade implementations.
4. Delivery & Operations
- Oversee project timelines, deliverables, and resource allocation.
- Ensure robustness, security, and compliance of deployed AI systems.
- Establish monitoring and feedback mechanisms to ensure continuous model performance in production.
- Manage risks, data governance, and ethical AI considerations.
Required Qualifications
- Education: Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Applied Mathematics, or related field. PhD preferred for research-intensive roles.
Experience:
- 610+ years of experience in software engineering, data science, or ML engineering.
- 24 years of experience leading AI/ML teams or projects.
Technical Skills:
- Proficiency in Python, SQL, and ML frameworks (TensorFlow, PyTorch, scikit-learn, etc.)
- Deep understanding of supervised/unsupervised learning, NLP, computer vision, or time series forecasting.
- Strong grasp of data structures, algorithms, and software design principles.
- Experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
- Familiarity with MLOps tools (MLflow, Kubeflow, Airflow, Prefect).
- Hands-on experience with APIs, microservices, and deployment pipelines.
Soft Skills:
- Excellent communication skills for both technical and non-technical audiences.
- Strong problem-solving, analytical, and decision-making abilities.
- Leadership, mentoring, and stakeholder management skills
Preferred Qualifications
- Experience with large language models (LLMs), generative AI, or prompt engineering.
- Experience integrating AI systems with enterprise products or SaaS platforms.
- Background in data governance, model explainability (XAI), and responsible AI practices.
- Contributions to open-source AI/ML projects or publications in reputed conferences/journals.
Key Performance Indicators (KPIs)
- Quality and accuracy of deployed models (e.g., precision, recall, ROI impact).
- Efficiency of AI delivery pipelines and model deployment frequency.
- Team skill growth and retention.
- Business value generated from AI solutions.
- Reduction in time-to-market for AI/ML initiatives.
