Description

You will define the AI/ML strategy, build autonomous agents, and lead the development and deployment of generative and classical ML models.

This role is hybrid.

Responsibilities

• Define the AI/ML strategy and roadmap aligned with product vision, prioritizing use cases in classical ML, Generative AI, and Agentic AI.
• Build, fine-tune, and deploy generative models (GPT, Stable Diffusion) and autonomous agents using LLMs (GPT, Claude, LLaMA) and multi-modal architectures.
• Design and implement end-to-end MLOps pipelines for model training, testing, deployment, monitoring, and retraining using CI/CD workflows.
• Optimize models for performance, scalability, and cost-efficiency while establishing robust version control for datasets, models, and code.
• Mentor and upskill team members, staying current with advancements in ML Ops, prompt engineering, and agentic frameworks.

Required Skills

• 8+ years of experience in AI/ML development and engineering.
• Proficiency in Python and libraries: NumPy, Pandas, Scikit-learn, Matplotlib.
• Hands-on experience with AI/ML frameworks: TensorFlow, PyTorch, Keras, Hugging Face, OpenAI APIs.
• Experience with LangChain modules (Chains, Memory, Tools, Agents) and Agentic AI frameworks.
• Strong understanding of machine learning algorithms, deep learning architectures, and reinforcement learning.
• Experience with MLOps tools: MLflow, Kubeflow, TFX, SageMaker.
• Proficiency in CI/CD tools: Jenkins, GitHub Actions, or GitLab CI for AI/ML workflows.
• Expertise in deploying models on cloud platforms: AWS, Azure, GCP.
• Experience with feature engineering, model evaluation, and hyperparameter tuning.

Key Skills

Education

Any Gradute