GenAI 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 busines 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 explain ability
Integrate GenAI into enterprise workflows (chatbots, copilots, knowledge assistants) as appliable 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)