GenAI Designer & Developer

MUST HAVE

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

Enable enterprise-grade features such as knowledge grounding using enterprise data

Enable enterprise-grade features such as 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

Additional Good to Have

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, monitoring, AI OPS, AI governance elements Responsible AI, fairness, and explain ability

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)

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