Mid-Level AI Platform Engineer

Jobtailor; California (MO); On-site; USD 150,000 - 190,000; Full time

Job summary

Jobtailor seeks a senior GenAI software engineer to develop GenAI applications using LLMs and RAG architectures. You will design scalable pipelines and vector databases, and implement prompt engineering to drive agentic AI workflows.

Join a team applying LangChain/LangGraph and cloud deployments on Azure and AWS, with focus on observability, security, and production readiness of GenAI workloads.

Qualifications

• Bachelor's degree in CS, engineering, or related field.
• 5–8 years in software or platform engineering.
• 2+ years hands-on GenAI systems experience with LLMs and RAG.
• Experience with cloud platforms (Azure and/or AWS).
• Proficiency in Python and distributed systems design.
• Experience deploying GenAI/ML solutions in production.
• Familiar with observability/monitoring tools and AI governance.

Responsibilities

• Develop and implement GenAI applications leveraging LLMs.
• Design and implement RAG architectures and vector databases.
• Prompt engineering and agentic AI workflows.
• Build intelligent pipelines using LangChain, LangGraph, and Microsoft Foundry Agent Service.
• Evaluate performance, accuracy, and scalability of GenAI solutions.
• Contribute to end-to-end GenAI lifecycle including design and development.
• Integration and deployment of GenAI systems.
• Performance tuning and optimization.
• Support secure deployment, scaling, and observability of GenAI workloads.
• Develop monitoring, logging, and observability practices for production environments.
• Develop and deploy GenAI systems across Azure and AWS.

Skills

GenAI concepts

LLMs (large language models)

Vector databases

Python development

Education

Bachelor’s degree in Computer Science, Engineering, or related field

Tools

Docker

Kubernetes

Terraform

LangChain

LangGraph

Azure

AWS

ARM/Bicep

Job description

Responsibilities

• Develop and implement GenAI applications leveraging Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG) architectures
• Prompt engineering techniques
• Agentic AI concepts and workflows
• Build intelligent pipelines using frameworks such as LangChain, LangGraph, and Microsoft Foundry Agent Service
• Evaluate solution performance, accuracy, and scalability of GenAI implementations
• Contribute to the end-to-end GenAI lifecycle, including Solution design and development
• Integration and deployment
• Performance tuning and optimization
• Support secure deployment, horizontal scaling, and operational stability of GenAI workloads
• Assist in implementing monitoring, logging, and observability practices for production environments
• Develop and deploy GenAI systems across cloud platforms (Azure and AWS)
• Contribute to distributed system design for scalable AI workloads
• Utilize modern infrastructure practices: Containerization (Docker)
• Orchestration (Kubernetes)
• Infrastructure as Code (Terraform, ARM/Bicep)

Qualifications

• Bachelor’s degree in Computer Science, Engineering, or related field
• 5–8 years of experience in software or platform engineering
• 2+ years hands‑on experience with GenAI systems, including LLMs and RAG architectures and vector databases
• Understanding of agentic AI concepts and exposure to frameworks such as LangChain or LangGraph
• Experience with cloud platforms (Azure and/or AWS)
• Knowledge of distributed systems and scalable application design
• Proficiency in Python development
• Experience with Docker, Kubernetes, and Infrastructure as Code tools
• Experience deploying GenAI or ML solutions in production environments
• Familiarity with observability and monitoring tools
• Understanding of AI governance, compliance, and security practices
• Experience in financial services or other regulated industries is a plus