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Generative AI Engineer – AI Platform & Agentic AI
DynPro Inc.
3 hours ago
Full-time
On-site
San Francisco, California, United States
We are seeking a hands-on Generative AI Engineer to design, develop, deploy, and support enterprise-scale AI applications and platforms. The ideal candidate will have strong experience with LLMs, AI Agents, Python, LiteLLM, LangChain/LangGraph, AWS, and AI/ML infrastructure.
This role focuses on building scalable AI platforms, developing agentic workflows, integrating enterprise AI tools, and ensuring the reliability, security, observability, and cost efficiency of production AI systems.
Key Responsibilities
Design, develop, and maintain enterprise Generative AI applications, LLM infrastructure, and AI platforms.
Build and manage LLM gateways (LiteLLM), including model routing, caching, fallbacks, cost tracking, and guardrails.
Develop AI agents and agentic workflows using LangGraph, CrewAI, or similar frameworks.
Integrate MCP (Model Context Protocol) servers with enterprise tools, APIs, databases, Slack, and Jira.
Implement LLM observability and monitoring, including response quality, token usage, latency, hallucination detection, and cost optimization.
Build LLM evaluation frameworks, including golden datasets, LLM-as-judge, regression testing, and quality metrics.
Develop and support RAG pipelines, vector databases, embeddings, and retrieval optimization.
Support enterprise AI platforms such as Glean, Gemini Enterprise, or Claude Cowork.
Manage AWS cloud infrastructure, Terraform, and CI/CD pipelines.
Collaborate with engineering, data science, and product teams to deliver production-ready AI solutions.
Required Skills & Qualifications
5+ years of experience in Software Engineering, AI/ML Engineering, Platform Engineering, or DevOps.
Strong hands-on Python programming, backend development, and REST API integration.
Production experience with LLMs, LLM APIs, prompt engineering, function calling, and tool integration.
Experience with LiteLLM, LangChain, LangGraph, or similar LLM orchestration frameworks.
Hands-on experience developing and deploying AI agents and agentic workflows.
Experience with LLM evaluation, testing, and monitoring.
Familiarity with enterprise AI platforms such as Glean, Gemini Enterprise, or similar solutions.
Strong experience with AWS, Terraform, and CI/CD tools such as GitHub Actions, Jenkins, or Azure DevOps.
Understanding of scalable AI infrastructure, troubleshooting, and production deployment.
Preferred Qualifications
Experience building or integrating MCP servers.
Experience with LLM observability tools such as LangSmith, Arize, Braintrust, or Datadog.
Hands-on knowledge of RAG, vector databases, embeddings, and semantic search.
Experience implementing LLM guardrails, security, and prompt injection protection.
Knowledge of Amazon Bedrock, AWS Lambda, and serverless AI architectures.
Experience with LLM cost optimization, semantic caching, and intelligent model routing.
Background working in enterprise SaaS or large-scale software environments.
Ideal Candidate A hands-on GenAI / AI Platform Engineer who has built and supported production LLM applications, AI agents, and AI infrastructure, with strong Python, cloud engineering, and AI platform integration experience.
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This role focuses on building scalable AI platforms, developing agentic workflows, integrating enterprise AI tools, and ensuring the reliability, security, observability, and cost efficiency of production AI systems.
Key Responsibilities
Design, develop, and maintain enterprise Generative AI applications, LLM infrastructure, and AI platforms.
Build and manage LLM gateways (LiteLLM), including model routing, caching, fallbacks, cost tracking, and guardrails.
Develop AI agents and agentic workflows using LangGraph, CrewAI, or similar frameworks.
Integrate MCP (Model Context Protocol) servers with enterprise tools, APIs, databases, Slack, and Jira.
Implement LLM observability and monitoring, including response quality, token usage, latency, hallucination detection, and cost optimization.
Build LLM evaluation frameworks, including golden datasets, LLM-as-judge, regression testing, and quality metrics.
Develop and support RAG pipelines, vector databases, embeddings, and retrieval optimization.
Support enterprise AI platforms such as Glean, Gemini Enterprise, or Claude Cowork.
Manage AWS cloud infrastructure, Terraform, and CI/CD pipelines.
Collaborate with engineering, data science, and product teams to deliver production-ready AI solutions.
Required Skills & Qualifications
5+ years of experience in Software Engineering, AI/ML Engineering, Platform Engineering, or DevOps.
Strong hands-on Python programming, backend development, and REST API integration.
Production experience with LLMs, LLM APIs, prompt engineering, function calling, and tool integration.
Experience with LiteLLM, LangChain, LangGraph, or similar LLM orchestration frameworks.
Hands-on experience developing and deploying AI agents and agentic workflows.
Experience with LLM evaluation, testing, and monitoring.
Familiarity with enterprise AI platforms such as Glean, Gemini Enterprise, or similar solutions.
Strong experience with AWS, Terraform, and CI/CD tools such as GitHub Actions, Jenkins, or Azure DevOps.
Understanding of scalable AI infrastructure, troubleshooting, and production deployment.
Preferred Qualifications
Experience building or integrating MCP servers.
Experience with LLM observability tools such as LangSmith, Arize, Braintrust, or Datadog.
Hands-on knowledge of RAG, vector databases, embeddings, and semantic search.
Experience implementing LLM guardrails, security, and prompt injection protection.
Knowledge of Amazon Bedrock, AWS Lambda, and serverless AI architectures.
Experience with LLM cost optimization, semantic caching, and intelligent model routing.
Background working in enterprise SaaS or large-scale software environments.
Ideal Candidate A hands-on GenAI / AI Platform Engineer who has built and supported production LLM applications, AI agents, and AI infrastructure, with strong Python, cloud engineering, and AI platform integration experience.
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