Description :

Overview :

CACTUS is a remote-first organization and we embrace an accelerate from anywhere culture.

You may be required to travel to our Mumbai office based on business requirements or for company/team events.

We are looking for an AI/ML Lead to drive the design, development, and deployment of scalable AI solutions.

In this role, you will lead end-to-end model development, guide engineering teams, and define best practices for building high-performance, production-ready systems.

If you enjoy leading teams, solving complex AI challenges, and shaping impactful, responsible AI solutions at scale, this role offers strong ownership and growth.

Responsibilities :

- Lead the design, prototyping, and deployment of AI/ML models.

- Develop reusable model components for classification, prediction, summarization, or image analytics.

- Evaluate open-source and proprietary AI models for fitment to large datasets.

- Define model development standards, performance benchmarks, and testing protocols.

- Collaborate with MLOps teams to automate model deployment, retraining, and monitoring workflows.

- Guide Data Scientists in experiment setup, hyperparameter tuning, and model validation.

- Conduct technical reviews and ensure all models meet Responsible AI and data privacy requirements.

- Mentor engineering teams in ML algorithms, model evaluation techniques, and ethical AI practices.

Requirements :

- B.Tech / M.Tech / M. / Ph. in Computer Science, Artificial Intelligence, or related fields

- Advanced certifications in AI/ML (e.g., TensorFlow Developer, AWS ML Engineer) preferred.

- Research papers, case studies, or significant open source contributions(are preferred)

- 7-10 years of overall professional experience in AI/ML solution development.

- At least 4-5 years leading AI/ML model implementation teams.

- Proven experience delivering end-to-end AI/ML use cases from prototyping to production deployment.

Technical Competencies :

- AI/ML Expertise : Deep learning architectures (CNN, RNN, Transformers), reinforcement learning, transfer learning, and foundation model fine-tuning

- Model Development : TensorFlow, PyTorch, Hugging Face, MLflow, advanced hyperparameter tuning, and neural architecture search

- Programming Languages : Python (expert level), R for statistical modeling, C++ for performance optimization, CUDA for GPU programming