Generative AI Professional

Job description

Educational Requirements Bachelor of Engineering,BTech,BSc,BCA,MTech,MCA,MSc Service Line Data Analytics Unit 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