GenAI Solution Design & DevelopmentDesign and build LLM-powered applications using RAG, embeddings, and vector search architecturesDevelop 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 orchestrationBuild reusable AI components (agents, pipelines, guardrails) to accelerate solution deliveryCopilot & AI Agent DevelopmentDevelop and customize copilots using Microsoft Copilot Studio / Azure FoundryIntegrate copilots with enterprise systems (ERP, CRM, ServiceNow, APIs)Design conversational workflows, triggers, and automation actionsEnable enterprise-grade features such as:Role-based access and identity integrationKnowledge grounding using enterprise dataResponsible AI guardrails (toxicity, hallucination control)Snowflake Cortex / Data AI EngineeringDevelop AI-powered applications using Snowflake Cortex AI functions and SnowparkImplement vector search, semantic models, and AI-driven analytics workflowsIntegrate structured and unstructured data pipelines to support AI modelsBuild self-service AI capabilities on data platforms with governance and cost optimizationAI/ML Engineering & MLOpsBuild and deploy models using Azure OpenAI, AWS Bedrock, or similar platformsCreate scalable pipelines for:Model deploymentMonitoring and observabilityContinuous improvement loopsGood to have:Implement AI guardrails, evaluation frameworks, and feedback loops for production systemsSDLC Automation with GenAILeverage tools like GitHub Copilot for:Code generation, test automation, debugging, and documentationAutomate SDLC activities using GenAI (requirements ? code ? testing ?
deployment)Enable developer productivity improvements and automation-first engineeringGenAI/LLM solutions (RAG, vector databases, prompt orchestration)Align business priorities with AI outcomes with tangible outcomes and optimizationsDefine and curate strategy for Model training, inference, and monitoring, AI OPS, AI governance elements Responsible AI, fairness, and explainabilityIntegrate GenAI into enterprise workflows (chatbots, copilots, knowledge assistants) as applicable and adoptable for relevant business operations architecting solutions across Azure, AWSManage AI/Ops and related governance from data collection to retraining and monitoring model driftsTechnical Skills:Hands on knowledge of data models, SQL, data lifecycle managementStrong 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)