Generative AI Engineer

Position Overview

We are seeking a highly skilled and innovative Generative AI Engineer to design, develop, deploy, and optimize AI-powered solutions that leverage Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and machine learning technologies. This role will be responsible for building intelligent applications, automating business processes, and integrating AI capabilities into products, services, and internal operations.

The ideal candidate has strong experience in AI/ML engineering, prompt engineering, LLM orchestration, cloud platforms, API integrations, and production-grade AI system deployment. This role requires a balance of software engineering expertise, data engineering knowledge, and applied AI problem-solving skills.

Location Requirement

To support collaboration, client engagement, and team alignment, candidates must currently reside in one of the following metropolitan areas in the United States:

• Dallas

• Houston

• Austin

• Atlanta

• Jacksonville

• Miami

• Nashville

• Charlotte

• Phoenix

Candidates residing outside of these locations will not be considered for this position.

Key Responsibilities

Generative AI Solution Development

• Design, develop, and deploy Generative AI applications and services

• Build AI-powered assistants, chatbots, copilots, and workflow automation tools

• Develop and optimize Retrieval-Augmented Generation (RAG) systems

• Implement prompt engineering strategies to improve model performance and reliability

• Fine-tune and evaluate AI models for specific business use cases

• Integrate foundation models into production applications and business processes

Large Language Models (LLMs) & AI Agents

• Work with leading AI models such as OpenAI, Anthropic, Google Gemini, Meta Llama, Mistral, and open-source LLMs

• Design and develop autonomous and semi-autonomous AI agents

• Build multi-agent systems for complex workflows and decision support

• Develop tool-calling, function-calling, and workflow orchestration capabilities

• Implement memory, context management, and conversational intelligence features

• Optimize AI agent performance, reliability, and cost efficiency

Knowledge Management & RAG Systems

• Design and maintain vector databases and semantic search systems

• Build document ingestion, indexing, and retrieval pipelines

• Implement enterprise knowledge management solutions using AI

• Improve retrieval quality through embeddings, chunking strategies, and ranking techniques

• Ensure AI-generated responses are accurate, relevant, and grounded in trusted data sources

• Monitor and optimize retrieval performance and response quality

AI Infrastructure & Cloud Engineering

• Deploy AI solutions using cloud platforms such as AWS, Azure, or GCP

• Design scalable and secure AI architectures

• Manage AI workloads, APIs, and model-serving infrastructure

• Implement CI/CD pipelines for AI applications and machine learning workflows

• Optimize infrastructure costs, scalability, and performance

• Collaborate with DevOps teams on deployment automation and monitoring

API Development & Systems Integration

• Develop APIs and backend services supporting AI applications

• Integrate AI systems with CRM, ATS, ERP, SaaS platforms, and internal systems

• Connect AI solutions to databases, business tools, and third-party services

• Develop secure data pipelines and application integrations

• Support enterprise-wide AI adoption initiatives

• Ensure seamless interoperability between AI systems and existing infrastructure

AI Evaluation, Monitoring & Optimization

• Develop evaluation frameworks for AI performance and quality

• Monitor model outputs, hallucinations, latency, and user satisfaction metrics

• Conduct testing, benchmarking, and validation of AI systems

• Implement observability and monitoring tools for production AI environments

• Continuously optimize prompts, workflows, and model configurations

• Establish AI governance and quality assurance processes

AI Security, Compliance & Responsible AI

• Implement security best practices for AI applications and data handling

• Ensure compliance with organizational policies and applicable regulations

• Manage access controls, privacy requirements, and data protection standards

• Identify and mitigate AI risks, bias, and misuse scenarios

• Support responsible AI initiatives and governance frameworks

• Maintain auditability and transparency of AI systems

Cross-Functional Collaboration & Leadership

• Collaborate with Product Managers, Software Engineers, Data Engineers, and Business Stakeholders

• Translate business requirements into scalable AI solutions

• Participate in AI strategy, architecture, and technology selection decisions

• Mentor junior engineers and contribute to AI best practices

• Support AI innovation initiatives and proof-of-concept projects

• Stay current with emerging AI technologies, research, and industry trends

Qualifications

Required

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, or a related field

• 3+ years of software engineering experience and 2+ years working with AI/ML technologies

• Strong proficiency in Python and modern software development practices

• Experience working with Large Language Models (LLMs) and Generative AI platforms

• Experience with prompt engineering and AI workflow design

• Strong understanding of APIs, backend development, and system integration

• Experience with vector databases and retrieval systems

• Knowledge of cloud platforms (AWS, Azure, or GCP)

• Strong analytical, troubleshooting, and problem-solving skills

• Excellent communication and collaboration abilities

• Must currently reside in one of the approved locations listed above

Preferred (Nice-to-Have)

• Experience with OpenAI, Anthropic, Gemini, Llama, Mistral, or similar AI platforms

• Experience building AI agents and multi-agent systems

• Knowledge of LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, or similar frameworks

• Experience with RAG architecture and semantic search systems

• Experience with fine-tuning, model evaluation, and reinforcement learning techniques

• Knowledge of vector databases such as Pinecone, Weaviate, Chroma, Qdrant, or Milvus

• Experience with Docker, Kubernetes, and containerized deployments

• Familiarity with MLOps and AI lifecycle management

• Experience in enterprise AI implementation projects

• Knowledge of AI governance, compliance, and responsible AI frameworks

Key Performance Indicators (KPIs)

AI Solution Delivery

• Number of AI solutions successfully deployed

• Project delivery timelines and milestone completion

• AI adoption and utilization rates

• Stakeholder satisfaction scores

Model & Application Performance

• AI response accuracy and relevance

• Hallucination reduction rate

• System uptime and reliability

• API latency and performance metrics

RAG & Knowledge Systems

• Retrieval accuracy and relevance scores

• Search success rates

• Knowledge base coverage and freshness

• User satisfaction with AI-generated responses

Operational Efficiency

• Business process automation improvements

• Time savings generated through AI solutions

• Cost optimization achieved through AI implementation

• Reduction in manual operational workload

Security & Compliance

• Compliance audit results

• Security incident prevention metrics

• Data privacy and governance adherence

• Responsible AI implementation benchmarks

Reporting To

• Head of AI

• AI Engineering Manager

• Director of Engineering

• Chief Technology Officer (CTO)

• Chief AI Officer (CAIO)

Employment Type & Work Setup

• Full-Time

• Remote (Candidates must reside in approved locations)

• Hybrid opportunities may be available based on business needs

• Agile development environment (Scrum/Kanban)

• Participation in AI innovation, research, and proof-of-concept initiatives

Work Environment & Conditions

• Fast-paced, innovation-driven technology environment

• Collaborative teams across Engineering, Product, Data, and Operations

• Continuous learning and experimentation with emerging AI technologies

• Opportunity to build cutting-edge AI applications and enterprise AI solutions

• Strong focus on scalability, reliability, security, and measurable business impact

• Access to modern AI platforms, cloud infrastructure, and development tools