Gen AI - Engineering Lead
At a glance
Senior AI Engineering role at Fa Ewjt Saasfaprod1. United States · full-time · no salary stated · we estimate $161,745–$225,300 across the United States.
Fa Ewjt Saasfaprod1 has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Design enterprise-scale GenAI systems with LLMs and agentic frameworks, creating reusable patterns and reference architectures
- Build prompt controls, versioning, tests and safeguards to improve output reliability and reduce security and compliance risks
- Create orchestration pipelines, agents and workflow templates, including multi-agent systems for complex business needs
- Turn stakeholder needs into prototypes, then deliver production applications built for scale, security and reliability
- Measure model quality, speed and safety, then improve prompts, retrieval and workflows
What you bring
- At least eight years in software engineering, machine learning, data engineering or AI solution development
- Four or more years of hands-on work with generative AI, LLMs and foundation models
- A bachelor's or master's degree in computer science, artificial intelligence or another listed field
- Strong Python skills, plus experience with RAG, vector databases, embeddings and semantic search
Who this fits
This role suits a senior engineer who can lead GenAI work from early prototypes through production and establish engineering practices for prompts, evaluation and optimization. The position is hybrid in the United States.
From the employer
We are seeking a highly skilled Senior Generative AI Engineer Lead to drive the design, development, and deployment of enterprise-grade Generative AI solutions. The ideal candidate will have deep expertise in Large Language Models (LLMs), prompt engineering, AI orchestration frameworks, cloud-native AI architectures, and model evaluation methodologies.
This role will lead the end-to-end lifecycle of GenAI initiatives, from translating business requirements into AI-powered prototypes to delivering scalable, production-ready solutions. The candidate will also establish engineering best practices around prompt governance, model guardrails, benchmarking, and performance optimization.
Generative AI Solution Design
- Architect and implement enterprise-scale GenAI solutions using LLMs, foundation models, and agentic AI frameworks.
- Design reusable AI patterns, accelerators, and reference architectures to enable rapid solution development.
- Translate business problems into scalable AI workflows and production-ready proof-of-concepts.
- Drive AI platform modernization through adoption of emerging GenAI technologies and best practices.
Prompt Engineering & LLM Governance
- Develop and maintain sophisticated prompt engineering frameworks for controlled and reliable LLM outputs.
- Implement prompt versioning, prompt lifecycle management, and testing strategies.
- Design AI guardrails to mitigate hallucinations, bias, security risks, and compliance concerns.
- Establish best practices for prompt optimization, response consistency, and output quality management.
AI Workflow Orchestration & Automation
- Design and build scalable orchestration pipelines using frameworks such as LangGraph, LangChain, CrewAI, Semantic Kernel, or equivalent.
- Develop reusable AI components, tools, agents, and workflow templates for enterprise adoption.
- Implement multi-agent systems and autonomous workflows to support complex business use cases.
Prototyping & Business Enablement
- Partner with business stakeholders to identify high-value AI opportunities.
- Rapidly develop AI prototypes and MVP solutions using synthetic and enterprise datasets.
- Convert prototypes into production-ready applications adhering to scalability, security, and reliability standards.
Cloud & Data Engineering
- Build scalable GenAI architectures across AWS, GCP, and Snowflake platforms.
- Leverage cloud-native AI services including Amazon Bedrock, SageMaker, Vertex AI, Snowflake Cortex AI, and related ecosystems.
- Design robust RAG (Retrieval-Augmented Generation) architectures incorporating vector databases, embeddings, and semantic search.
- Optimize model deployment, inference performance, and infrastructure cost efficiency.
Evaluation & Performance Optimization
- Establish AI evaluation frameworks to measure accuracy, relevance, latency, safety, and business impact.
- Develop benchmarking methodologies for comparing prompts, models, and workflows.
- Define KPIs, observability frameworks, and monitoring strategies for GenAI applications.
- Continuously improve model performance through prompt tuning, retrieval optimization, and workflow enhancements.
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
- 8+ years of experience in software engineering, machine learning, data engineering, or AI solution development.
- 4+ years of hands-on experience with Generative AI, LLMs, and foundation models.
- Strong experience designing enterprise GenAI solutions utilizing advanced LLM architectures and prompt frameworks.
- Expertise in building scalable AI workflows, orchestration pipelines, and reusable AI components.
- Proven ability to translate business requirements into production-ready AI solutions and prototypes using synthetic data.
- Deep understanding of prompt versioning, prompt governance, guardrails, and controlled LLM outputs.
- Strong knowledge of AI evaluation frameworks, benchmarking methodologies, and optimization techniques.
- Experience with vector databases, embeddings, semantic search, and RAG architectures.
- Strong proficiency in Python and modern AI/ML development frameworks.