Minimum 5 years’ experience leading the design, development, and deployment of scalable AI/ML and GenAI solutions.

QUALIFICATIONS

• Minimum 5 years’ experience architecting and deploying scalable AI/ML and GenAI solutions in enterprise environments.
• Deep expertise in machine learning, deep learning, and generative AI technologies, including hands-on experience with frameworks like TensorFlow, PyTorch , and modern LLM orchestration tools.
• Strong familiarity with cloud platforms ( AWS, Azure, GCP ) and MLOps practices for end-to-end machine learning lifecycle management.
• Demonstrated leadership in managing agile, cross-functional teams and collaborating with stakeholders.
• Significant experience in prompt engineering and prompt design for LLMs and GenAI applications.
• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field; advanced degrees or certifications (e.g., Azure AI Engineer) are advantageous.
• Experience with personalization, recommendation systems, or conversational AI is highly desirable.

• Architect and develop scalable GenAI pipelines, APIs, and microservices for real-time and batch AI applications using frameworks such as FastAPI, Ray, or LangServe.
• Design robust prompt strategies for instruction-following, reasoning, and multi-turn conversations, with a focus on RAG architectures for personalized, domain-specific use cases.
• Lead embedding model selection and tuning to optimize semantic search and RAG performance.
• Oversee LLM Ops workflows, including model orchestration, evaluation, deployment, rollback strategies, and monitoring in production environments.
• Drive model fine-tuning efforts to customize LLMs for proprietary datasets and regulated industries.
• Establish and govern AI testing frameworks, covering functional testing, regression testing, hallucination detection, safety filters, and output quality assessment.
• Implement enterprise-grade observability, lineage tracking, and CI/CD automation using tools such as MLflow, Databricks, Azure ML, or Vertex AI .
• Lead continuous improvement initiatives based on telemetry, user feedback, and cost-performance trade-offs.
• Demonstrate expertise in Python , with deep proficiency in GenAI frameworks, vector search systems, and MLOps toolchains.