GenAI Designer & Developer

TechDigital Group; Dallas (TX); On-site; USD 90,000 - 130,000; Full time

Job summary

TechDigital Group in Dallas, Texas, is seeking a skilled engineer to develop and implement LLM-powered applications. You will be responsible for designing AI assistants, implementing data pipelines, and integrating with various enterprise systems.

The ideal candidate will have strong experience with AI/ML algorithms, cloud platforms, and programming languages like Python. This role requires innovation in developing scalable AI solutions and enhancing business automation through technology.

Responsibilities

• Design and build LLM-powered applications using RAG and vector search architectures.
• Develop and customize copilots using Microsoft Copilot Studio / Azure Foundry.
• Integrate copilots with enterprise systems such as ERP and CRM.
• Engineer end-to-end GenAI pipelines including prompt engineering and response orchestration.
• Build self-service AI capabilities on data platforms with governance.

Skills

LLM-powered application development

Copilot-based AI development

GenAI pipeline engineering

AI/ML algorithms

Data lifecycle management

Cloud platforms (AWS, Azure)

Tools

Snowflake

Azure OpenAI

AWS Bedrock

SQL

Python

PySpark

Job description

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)