Senior GenAI Engineer

Educational Requirements

• Bachelor of Engineering,BCS,BBA,BCom,MCA,MSc

Service Line Responsibilities

• Generative AI Development
• Design and implement enterprise GenAI applications using LLMs.
• Develop multi-agent workflows and agentic AI systems using LangGraph.
• Build conversational AI, copilots, knowledge assistants, and intelligent automation solutions.
• Engineer prompts and workflows to optimize model performance and user experience.
• RAG Knowledge Systems
• Design and implement Retrieval-Augmented Generation (RAG) pipelines.
• Integrate vector databases and document retrieval frameworks.
• Optimize document chunking, embeddings, indexing, retrieval, and reranking strategies.
• Improve response accuracy, latency, and relevance of AI systems.

Additional Responsibilities

• Deployment Productionization
• Deploy and serve models using MLflow and Databricks.
• Monitor model performance, drift, and operational metrics.
• Implement CI/CD pipelines for AI and machine learning workloads.
• Ensure scalability, reliability, and observability of AI services.
• Leadership Mentorship
• Mentor junior engineers and AI practitioners.
• Conduct design reviews and architecture discussions.
• Define GenAI development standards and best practices.
• Collaborate with business stakeholders to identify AI use cases and translate them into solutions.

Technical and Professional Requirements

• AI Platform Engineering
• Build scalable AI platforms on Databricks and cloud environments.
• Develop model lifecycle management solutions using MLflow.
• Implement model tracking, versioning, deployment, monitoring, and governance.
• Support MLOps and LLMOps initiatives.
• Data Engineering
• Build and optimize large-scale data pipelines using PySpark.
• Process structured and unstructured data for AI and analytics workloads.
• Collaborate with data engineering teams to build feature and knowledge pipelines.
• Ensure data quality, lineage, and governance standards.

Preferred Skills

• Retrieval-Augmented Generation (RAG)
• LangGraph
• LLMs
• Generative AI
• Databricks
• MLflow
• PySpark
• CI/CD
• MLOps/LLMOps