Core Responsibilities

• Lead the end-to-end design and development of AI/ML & Generative AI solutions, from ideation to deployment.
• Build and optimize data pipelines and architectures to support scalable AI models.
• Develop, fine-tune, and optimize models using ML/GenAI frameworks such as PyTorch, TensorFlow, and Hugging Face.
• Apply prompt engineering techniques and fine-tuning strategies for LLMs and domain-specific use cases.
• Leverage cloud-based AI services (Azure OpenAI, AWS SageMaker, Google Vertex AI) for scalable solution deployment.

Secondary Responsibilities

• Implement MLOps best practices using MLflow, Docker, and Kubernetes for model lifecycle management.
• Provide technical leadership through code reviews and mentoring junior engineers.
• Collaborate with cross-functional teams and manage stakeholders to align AI solutions with business objectives.

Must-Have Skills

• Proven expertise in AI/ML solution design and production deployment.
• Hands-on experience with PyTorch, TensorFlow, Hugging Face, and related frameworks.
• Strong knowledge of data pipeline architecture and cloud AI platforms (Azure, AWS, GCP).
• Experience in prompt engineering and fine-tuning models.

Good-to-Have Skills

• Strong problem-solving and communication skills.
• Familiarity with MLOps tools (MLflow, Docker, Kubernetes).
• Experience in team leadership, mentoring, and stakeholder management.