Hiring for AI Engineer- Santa Clara, CA- 5 days onsite

Tech Mirrors; Santa Clara (CA); On-site; USD 140,000 - 210,000; Full time

Job summary

Tech Mirrors in Santa Clara, CA is seeking an AI Engineer to build production-grade agentic AI systems for enterprise use. You will develop multi-agent workflows, integrate LLMs, and support deployment and automation to run reliably and securely in production.

This is a hands-on engineering engagement focusing on agents, orchestration logic, and supporting infrastructure that handles real production workloads, not proof-of-concept or advisory work.

Qualifications

• 3-5 years in Machine Learning, AI or related field with production systems.
• At least 1 year building custom agentic AI applications.
• Strong Python skills and modern development practices.
• Hands-on experience with LLMs and prompt engineering.
• Experience building AI agents with LangGraph.
• Experience with enterprise cloud AI platforms and cloud deployment.
• REST APIs, WebSockets, and event-driven systems knowledge.
• CI/CD tooling (Jenkins) and Git version control.
• Excellent written and verbal communication; independent in cross-functional teams.
• Data layer integration with source systems for agentic apps.

Responsibilities

• Build AI agents and multi-agent systems using LangGraph and LangChain tools.
• Develop and tune prompt engineering workflows across multiple LLMs.
• Develop REST APIs, WebSocket services, and event-driven pipelines for real-time AI services.
• Automate testing and releases through Jenkins CI/CD, and maintain code/docs with Git/Jira/Confluence.
• Deployment of AI applications in enterprise with best practices.
• Coordinate with platform, security, and product teams for scalable deployments.

Skills

Machine Learning

AI Systems

Python

LLMs

Agentic AI

Tools

LangGraph

LangChain

Jenkins

Git

Confluence

Job description

AI Engineer- Santa Clara, CA- 5 days onsite

Client Job Description: AI Engineer

AI Engineer to build and deliver production-grade agentic AI systems
for enterprise use. The engineer will develop multi-agent workflows,
integrate large language models into existing enterprise systems, and
support the deployment and automation needed to run them reliably and
securely in production.
This is a hands-on engineering engagement. The work centers on
building agents, orchestration logic, and supporting infrastructure
that performs under real production workloads, not on proof-of-concept
or advisory work.

Scope of Work

• Build AI agents and multi-agent systems using frameworks with
  LangGraph and LangChain tools.
• Develop and tune prompt engineering workflows across multiple LLMs
  (GPT, Claude, LLaMA), balancing reliability, cost, and latency.
• Develop REST APIs, WebSocket services, and event-driven pipelines
  for real-time AI services that remain stable under load.
• Automate testing and releases through Jenkins CI/CD, and maintain
  code and documentation standards using Git, Jira, and Confluence.
• Deployment of AI Application in enterprise adhering to best practices
• Use AI-augmented development tools such as Claude Code and Codex to
  accelerate delivery.
• Coordinate with platform, security, and product teams to deliver
  scalable, secure deployments.

Must-Have Skills

• 3-5 years in Machine Learning, AI, or a related field, with
  production systems delivered.
• At least 1 year building custom Agentic AI applications
• Strong Python skills and sound modern development practices.
• Hands-on experience with LLMs and prompt engineering across the full
  application lifecycle.
• Demonstrated experience building AI agents with LangGraph.
• Familiarity with at least one enterprise cloud AI platform for
  building and deploying agentic applications, such as Azure AI Foundry,
  AWS Bedrock, or Google Gemini Enterprise, including cloud-native
  deployment practices.
• Working knowledge of REST APIs, WebSockets, and event-driven systems.
• Proficiency with CI/CD tooling (Jenkins) and version control (Git).
• Fluency with AI-augmented development tools for rapid prototyping.
• Strong written and verbal communication, an analytical approach to
  problem-solving, and the ability to work independently within a
  cross-functional team.
• Data layer curations and integration with source system for agentic
  application

Good-to-Have Skills

• Familiarity with Databricks.
• Exposure to MLOps/LLMOps workflows and application monitoring.
• Knowledge of enterprise security, compliance, and governance for AI systems.
• Familiarity with code and model lifecycle management practices.