GenAI Solution Designer & Developer

MUST HAVE

​

GenAI Solution Design & Development

​

• Design and build LLM-powered applications using RAG, embeddings, and vector search architectures
• Develop Copilot-based AI assistants and agents for enterprise use cases (automation, Q&A, workflow orchestration)
• Engineer end-to-end GenAI pipelines including prompt engineering, context handling, and response orchestration
• Build reusable AI components (agents, pipelines, guardrails) to accelerate solution delivery

​

Copilot & AI Agent Development

​

• Develop and customize copilots using Microsoft Copilot Studio / Azure Foundry
• Integrate copilots with enterprise systems (ERP, CRM, ServiceNow, APIs)
• Design conversational workflows, triggers, and automation actions
• Enable enterprise-grade features such as:
• Role-based access and identity integration
• Knowledge grounding using enterprise data
• Responsible AI guardrails (toxicity, hallucination control)

​

Snowflake Cortex / Data AI Engineering

​

• 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 with governance and cost optimization

​

AI/ML Engineering & MLOps

​

• Build and deploy models using Azure OpenAI, AWS Bedrock, or similar platforms

​

Create Scalable Pipelines For

​

• Model deployment
• Monitoring and observability
• Continuous improvement loops

​

GOOD TO HAVE

​

Implement AI guardrails, evaluation frameworks, and feedback loops for production systems

​

• SDLC Automation with GenAI
• Leverage tools like GitHub Copilot for:
• Code generation, test automation, debugging, and documentation
• Automate SDLC activities using GenAI (requirements code testing deployment)
• Enable developer productivity improvements and automation-first engineering
• GenAI/LLM solutions (RAG, vector databases, prompt orchestration)
• Align business priorities with AI outcomes with tangible outcomes and optimizations
• Define and curate strategy for Model training, inference, and monitoring, AI OPS, AI governance elements Responsible AI, fairness, and explainability
• Integrate GenAI into enterprise workflows (chatbots, copilots, knowledge assistants) as applicable and adoptable for relevant business operations architecting solutions across Azure, AWS
• Manage AI/Ops and related governance from data collection to retraining and monitoring model drifts

​

Technical Skills

​

• Hands-on knowledge of data models, SQL, 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)