Generative AI

Key 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
• 3 5 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
• Bachelor s degree in engineering or computers or AI

Technical Requirements:

• Technology AI ML Python Gen AI Databricks

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

Preferred Skills:

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