Industry: Enterprise AI, Generative AI Platforms, Machine Learning Infrastructure, Cloud Software, AI Automation, Data Intelligence, and Enterprise SaaS
Location: USA Remote
Compensation Package: $280K – $359K
About the Organization
We are a large, enterprise-scale AI, generative AI platform, machine learning infrastructure, cloud software, automation, data intelligence, and enterprise SaaS organization building advanced AI-enabled products for enterprise customers across the United States. The company develops secure, scalable, and business-ready AI systems that help organizations automate workflows, improve decision-making, enhance customer experiences, generate insights, accelerate knowledge work, and integrate intelligent capabilities into modern digital operations.
This is not a traditional software engineering role focused only on backend services, application features, or model API usage. The Generative AI Engineer will help design, build, evaluate, and deploy AI-powered systems that combine large language models, retrieval pipelines, prompt engineering, data workflows, model evaluation, application engineering, responsible AI practices, and production-grade infrastructure. This role will connect research-informed AI development with practical enterprise product delivery.
The company is continuing to invest in LLM-powered applications, retrieval-augmented generation, AI agents, workflow automation, enterprise knowledge systems, model evaluation frameworks, prompt optimization, AI safety controls, vector search, customer-specific AI solutions, multimodal capabilities, secure data access, and scalable AI infrastructure. As the organization grows, leadership is seeking a Generative AI Engineer who can bring strong software engineering ability, applied AI knowledge, experimentation discipline, product thinking, and technical creativity to a high-impact role.
The Generative AI Engineer will work across AI Engineering, Product, Data Science, Machine Learning, Platform Engineering, Security, Data Engineering, Customer Success, and business stakeholders to build AI features that are reliable, secure, measurable, and useful in real customer environments. This role will support model integration, prompt design, RAG architecture, AI workflow development, evaluation pipelines, observability, experimentation, performance optimization, and production deployment.
This role requires an engineer who understands that successful generative AI systems are not created by model access alone. The ideal candidate will know how to design the surrounding architecture, prepare and retrieve the right context, evaluate output quality, reduce hallucination risk, manage latency, protect customer data, build feedback loops, test prompts and models, and translate ambiguous business problems into practical AI-powered solutions.
The selected candidate will help improve how the company turns generative AI concepts into scalable enterprise products. This includes building reusable AI components, improving model evaluation, supporting secure AI integrations, strengthening prompt and retrieval quality, improving observability, reducing operational risk, and helping teams deliver AI capabilities that customers can trust.
This is a strong opportunity for an applied AI engineer who wants to build real-world generative AI systems, work with modern cloud and data platforms, partner with strong product and engineering teams, and help a large technology-driven organization deliver meaningful AI-powered business value.
Essential Duties and Responsibilities
• Design, build, test, and deploy generative AI applications, LLM-powered workflows, AI assistants, retrieval systems, automation features, and intelligent product capabilities.
• Develop and improve retrieval-augmented generation systems using vector databases, embeddings, semantic search, ranking, chunking strategies, metadata filtering, and enterprise knowledge sources.
• Build prompt engineering frameworks, prompt templates, system instructions, guardrails, reusable prompt components, and structured output workflows for production AI features.
• Integrate large language models and AI services from providers such as OpenAI, Anthropic, Google, AWS, Azure, open-source models, or internal model platforms.
• Partner with Product, Design, Data Science, Machine Learning, Engineering, and Customer Success teams to translate business problems into practical AI solution designs.
• Build evaluation frameworks to measure AI quality, accuracy, relevance, hallucination risk, safety, latency, cost, consistency, and customer usefulness.
• Develop backend services, APIs, orchestration layers, data pipelines, model interaction services, and application components that support AI-powered product experiences.
• Implement observability for AI systems, including prompt logs, model outputs, latency, token usage, retrieval quality, error rates, user feedback, cost monitoring, and performance metrics.
• Improve AI reliability through testing strategies, golden datasets, regression checks, human feedback loops, automated evaluations, and continuous improvement routines.
• Partner with Security, Legal, Privacy, and Compliance teams to support responsible AI practices, secure data handling, access controls, auditability, privacy requirements, and customer trust expectations.
• Optimize AI systems for performance, scalability, latency, cost efficiency, model selection, context quality, and production reliability.
• Support experimentation with new model capabilities, AI agents, tool use, function calling, multimodal inputs, synthetic data, fine-tuning where appropriate, and AI workflow automation.
• Document AI architecture, data flows, prompt logic, evaluation methods, model assumptions, risk controls, system dependencies, and implementation decisions.
• Stay current on generative AI research, LLM capabilities, model evaluation practices, AI safety methods, cloud AI tools, open-source frameworks, and production AI engineering patterns.
Job Qualifications and Requirements
• Bachelor’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, Mathematics, Statistics, or a related technical field required.
• Master’s degree or advanced technical education in AI, machine learning, computer science, natural language processing, data science, or distributed systems preferred.
• 4+ years of professional software engineering, machine learning engineering, AI engineering, data engineering, or applied technology development experience.
• 2+ years of hands-on experience building generative AI, LLM-powered applications, RAG systems, AI assistants, NLP systems, recommendation systems, or applied machine learning products preferred.
• Strong programming skills in Python, TypeScript, JavaScript, Java, Go, or similar languages, with strong preference for Python-based AI development experience.
• Experience working with LLM APIs, embeddings, vector databases, semantic search, prompt engineering, model evaluation, AI orchestration, and production AI workflows.
• Familiarity with tools and frameworks such as LangChain, LlamaIndex, Haystack, OpenAI API, Anthropic API, Hugging Face, PyTorch, TensorFlow, FastAPI, Flask, Node.js, or similar technologies preferred.
• Experience with vector databases or search systems such as Pinecone, Weaviate, Milvus, FAISS, Elasticsearch, OpenSearch, pgvector, Chroma, or similar platforms preferred.
• Experience with cloud platforms such as AWS, Azure, Google Cloud, Databricks, Snowflake, Kubernetes, Docker, serverless services, CI/CD, and observability tools preferred.
• Strong understanding of APIs, backend services, data pipelines, authentication, authorization, secure application design, testing, deployment, and production support.
• Experience with AI evaluation, prompt testing, model benchmarking, A/B testing, human feedback workflows, data quality checks, and quality assurance for AI systems.
• Ability to work with Product, Engineering, Data Science, Data Engineering, Security, Customer Success, and business stakeholders in a collaborative technical environment.
• Excellent problem-solving, technical communication, documentation, experimentation, and cross-functional collaboration skills.
Personal Capabilities and Qualifications
• Applied AI builder with the ability to turn ambiguous business problems into reliable, secure, and useful generative AI solutions.
• Strong software engineering mindset with the ability to build production-ready systems, not just prototypes or experiments.
• Curious and research-aware, with the ability to evaluate new AI methods, model capabilities, frameworks, and product possibilities without losing practical business focus.
• Data-quality focused, with the ability to understand how retrieval quality, context design, embeddings, prompts, and source data affect AI output.
• Product-minded and customer-centered, with the ability to build AI features that solve real user problems and support measurable outcomes.
• Security-aware and responsible, with the ability to handle sensitive data, access controls, privacy expectations, AI risks, and customer trust considerations.
• Analytical and experimental, with the ability to design evaluations, compare approaches, measure output quality, and improve systems through feedback.
• Collaborative and able to work effectively with engineers, product managers, data scientists, security teams, designers, customer-facing teams, and business leaders.
• Strong communicator who can explain AI behavior, limitations, risks, architecture decisions, and tradeoffs clearly to technical and non-technical audiences.
• High integrity and discretion when handling customer data, model outputs, proprietary prompts, internal datasets, product roadmaps, and confidential business use cases.
Strategic Support
The Generative AI Engineer will provide strategic support by helping the organization build AI-powered product capabilities that are secure, scalable, measurable, and aligned with enterprise customer needs.
This role will help Product, Engineering, Data Science, Security, Customer Success, and business teams make better decisions about AI architecture, model selection, retrieval design, prompt strategy, evaluation quality, production readiness, and responsible AI implementation. The Generative AI Engineer will ensure AI development is not managed as isolated experimentation, but as a disciplined engineering practice that creates trusted customer value.
Key areas of strategic support may include:
• Generative AI application development and production deployment.
• LLM integration, orchestration, and model provider evaluation.
• Retrieval-augmented generation architecture and optimization.
• Prompt engineering, prompt testing, and structured output design.
• AI evaluation frameworks and quality measurement.
• Vector search, embeddings, and enterprise knowledge retrieval.
• AI workflow automation and intelligent product experiences.
• Responsible AI, safety, privacy, and security partnership.
• Model performance, latency, cost, and scalability optimization.
• AI observability, feedback loops, and monitoring.
• Product experimentation and AI feature prototyping.
• Cross-functional AI solution design with Product, Engineering, and Data Science.
• Customer-specific AI use case support and technical advisory.
• Documentation of AI architecture, data flows, and risk controls.
The Generative AI Engineer will help ensure the company builds AI capabilities that are practical, trustworthy, and valuable for enterprise customers.
Working Conditions
• Location: USA Remote.
• Primarily remote technical role with regular collaboration across AI engineering, product, data science, platform engineering, security, customer success, and business teams.
• Flexibility required during AI feature launches, production incidents, model performance issues, customer escalations, security reviews, evaluation deadlines, or urgent product experiments.
• Occasional travel may be required for engineering offsites, AI strategy workshops, customer sessions, product planning meetings, company gatherings, or technical conferences.
• Regular collaboration with AI Engineering, Product, Data Science, Machine Learning, Platform Engineering, Data Engineering, Security, Legal, Privacy, Customer Success, Support, and business stakeholders.
• Fast-paced enterprise AI environment with high visibility around product innovation, AI reliability, customer trust, model performance, data quality, and production readiness.
• Role requires handling confidential customer data, proprietary prompts, AI workflows, model outputs, internal datasets, product plans, security-sensitive information, and technical architecture with discretion.
Job Function
• Generative AI Engineering
• LLM Application Development
• Retrieval-Augmented Generation
• AI Product Engineering
• Prompt Engineering
• AI Evaluation
• Machine Learning Engineering
• Vector Search
• AI Workflow Automation
• Cloud AI Development
• Data and AI Integration
• Responsible AI Engineering
• AI Observability
• Backend Engineering
• Enterprise AI Solutions
Compensation & Benefits
Compensation Package: $280K – $359K
The total compensation package may include base salary, performance bonus, AI engineering performance incentives, product innovation incentives, long-term incentives, equity participation where applicable, and additional benefits depending on experience, qualifications, and final role alignment.
Benefits may include:
• Comprehensive medical, dental, and vision coverage.
• Performance bonus eligibility.
• AI engineering and product innovation incentive opportunities.
• Long-term incentive opportunities.
• Equity or ownership-aligned compensation where applicable.
• Retirement savings plan with company contribution.
• Paid time off and company holidays.
• USA remote work flexibility.
• Visibility across AI engineering, product, data science, platform, security, and customer-facing teams.
• Professional development support for generative AI, LLM engineering, cloud AI platforms, machine learning, data infrastructure, AI safety, prompt engineering, and software architecture education.
• Wellness, employee assistance, and work-life support programs.
• Access to modern AI platforms, cloud infrastructure, vector databases, observability tools, developer platforms, collaboration systems, and enterprise engineering technologies.
Why Join Us
This is an opportunity to join a large technology-driven organization where generative AI engineering directly influences product innovation, customer outcomes, automation, knowledge access, enterprise productivity, and long-term business value.
The Generative AI Engineer will have the opportunity to build real-world AI systems, improve RAG pipelines, evaluate model quality, support responsible AI practices, and help shape intelligent product experiences used by enterprise customers.
You will work in an environment that values technical excellence, experimentation, product impact, responsible innovation, secure engineering, and measurable customer value. The company is investing in generative AI, cloud platforms, data intelligence, model evaluation, automation, AI safety, and scalable enterprise AI architecture.
For an AI engineer who enjoys building practical AI products, solving complex technical problems, improving model behavior, and turning generative AI into trusted business capability, this role offers the technical challenge, visibility, and platform to make a meaningful impact.