GenAI Solution Designer and Developer

• Design and build LLM-powered applications using RAG, embeddings, and vector search architectures.
• Develop Copilot-based AI assistants and agents for enterprise use cases.
• Engineer end-to-end GenAI pipelines including prompt engineering, context handling, and response orchestration.
• Build reusable AI components to accelerate solution delivery.
• Develop and customize copilots using Microsoft Copilot Studio / Azure Foundry.
• Integrate copilots with enterprise systems.
• Design conversational workflows, triggers, and automation actions.
• Enable enterprise-grade features such as role-based access and identity integration.
• Implement responsible AI guardrails.
• Develop AI-powered applications using Snowflake Cortex AI functions and Snowpark.
• Implement vector search, semantic models, and AI-driven analytics workflows.
• Integrate structured and unstructured data pipelines to support AI models.
• Build self-service AI capabilities on data platforms.
• Build and deploy models using Azure OpenAI, AWS Bedrock, or similar platforms.
• Create scalable pipelines for model deployment, monitoring, and observability.

Nice to Have:

• Implement AI guardrails, evaluation frameworks, and feedback loops for production systems.
• Leverage tools like GitHub Copilot for code generation, test automation, debugging, and documentation.
• Automate SDLC activities using GenAI.
• Enable developer productivity improvements and automation-first engineering.
• Align business priorities with AI outcomes.
• Define and curate strategy for model training, inference, and monitoring.
• Integrate GenAI into enterprise workflows.

• 1+ years of GenAI Solution Design & Development experience.
• Experience in designing and building LLM-powered applications using RAG, embeddings, and vector search architectures.
• Experience in developing Copilot-based AI assistants and agents for enterprise use cases such as automation, Q&A, and workflow orchestration.
• Experience in engineering end-to-end GenAI pipelines including prompt engineering, context handling, and response orchestration.
• Experience in building reusable AI components (agents, pipelines, guardrails) to accelerate solution delivery.
• Experience in developing and customizing copilots using Microsoft Copilot Studio / Azure Foundry.
• Experience in integrating copilots with enterprise systems (ERP, CRM, ServiceNow, APIs).
• Experience in designing conversational workflows, triggers, and automation actions.
• Experience in enabling enterprise-grade features such as role-based access and identity integration.
• Experience in knowledge grounding using enterprise data.
• Experience in implementing responsible AI guardrails (toxicity, hallucination control).
• Experience in developing AI-powered applications using Snowflake Cortex AI functions and Snowpark.
• Experience in implementing vector search, semantic models, and AI-driven analytics workflows.
• Experience in integrating structured and unstructured data pipelines to support AI models.
• Experience in building self-service AI capabilities on data platforms with governance and cost optimization.
• Experience in building and deploying models using Azure OpenAI, AWS Bedrock, or similar platforms.
• Experience in creating scalable pipelines for model deployment, monitoring and observability, and continuous improvement loops., * Hands-on knowledge of data models, SQL, and data lifecycle management.
• Strong knowledge of AI/ML algorithms, data structures, and performance optimization.
• Proficiency in programming languages such as Python, SQL, and PySpark.
• Experience with cloud platforms (AWS, Azure) and big data technologies (Spark, Snowflake).