Generative AI Professional

Educational Requirements Service Line Responsibilities

• Solution Delivery Consulting Partner with client stakeholders to understand business goals, translate them into AI/ML and Generative AI use cases, and define success metrics.
• Contribute to solution design, effort estimation, and delivery planning for AI initiatives in a consulting environment.
• Communicate findings, trade-offs, and recommendations through clear documentation and presentations.
• Generative AI Development Build Python-based prototypes and production-ready components for Generative AI workflows (prompting, evaluation, and iteration).
• Develop and refine prompts, templates, and guardrails to improve response quality, safety, and consistency.
• Implement evaluation approaches to measure output quality (accuracy, relevance, hallucination checks) and drive continuous improvement.
• AI/ML Engineering Develop and maintain ML pipelines in Python for data preparation, training, inference, and monitoring.
• Perform model experimentation, feature engineering, and performance tuning aligned to business requirements.
• Collaborate with cross-functional teams to integrate AI services into applications and workflows.
• Minimum Qualifications: 35 years of professional experience delivering Python-based solutions, including AI/ML or Generative AI components.
• Hands-on experience with Generative AI concepts and implementation (prompt engineering, evaluation, and iterative improvement).
• Working knowledge of AI/ML fundamentals (supervised/unsupervised learning, model validation, metrics).
• Strong Python programming skills with clean coding practices, testing, and debugging.
• Bachelors degree in engineering or computers or AI

Additional Responsibilities

• Preferred Qualifications: Experience delivering end-to-end AI/ML solutions in a client-facing or consulting setup, including requirement discovery and stakeholder management.
• Exposure to LLM application patterns such as RAG, embeddings, vector search, and tool/function calling.
• Familiarity with MLOps practices such as experiment tracking, model versioning, CI/CD for ML, and production monitoring.
• Experience with scalable data/ML platforms and workflows (e.g., Databricks-style notebook-to-production practices).
• Proven ability to balance rapid prototyping with production readiness, including performance, security, and reliability considerations.
• Good to have skills:RAG, Embeddings, Vector Databases, Prompt Engineering, MLOps

Technical and Professional Requirements

• Technology- >AI/ML, Python, Gen AI, Databricks

Preferred Skills

• Technology- >AI-AI Engineering- >AI/ML Solution Architecture and Design- >traditional ai ml
• Technology- >AI-Generative AI- >Prompt Engineering