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Hiring for AI Engineer- Santa Clara, CA- 5 days onsite
Tech Mirrors
4 hours ago
Full-time
On-site
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.
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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.
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