AI engineer

Robotics Technologies LLC
Sunnyvale, United States of America
28 days ago

Role details

Contract type
Temporary contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior

Job location

Sunnyvale, United States of America

Tech stack

Clean Code Principles
API
Artificial Intelligence
Amazon Web Services (AWS)
Azure
Continuous Integration
Python
Cloud Services
Software Engineering
SQL Databases
Pulumi
Google Cloud Platform
Large Language Models
Generative AI
Event Driven Architecture
Kubernetes
Deployment Automation
Machine Learning Operations
Terraform
GPT
Docker
Microservices

Job description

  • Lead the end-to-end deployment of GenAI applications for customers-from discovery to delivery.
  • Architect and implement robust, scalable solutions using Python, Langchain/LangGraph, and LLM frameworks.
  • Act as a trusted technical advisor to customers, understanding their needs and crafting tailored AI solutions.
  • Collaborate closely with product, ML, and engineering teams to influence roadmap and core platform capabilities.
  • Write clean, maintainable code and build reusable modules to streamline future deployments.
  • Operate across cloud platforms (AWS, Azure, GCP) to ensure secure, performant infrastructure.
  • Continuously improve deployment tools, pipelines, and methodologies to reduce time-to-value.

Requirements

Do you have experience in Software engineering?, * 10+ years of experience in software engineering or solutions engineering, ideally in a customer-facing capacity.

  • Proven expertise in Python, Langchain, LangGraph, and SQL.
  • Deep experience with engineering architecture, including APIs, microservices, and event-driven systems
  • Demonstrated success in designing and deploying GenAI applications into production environments
  • Strong proficiency with cloud services such as AWS, GCP, and/or Azure.
  • Excellent communication skills, with the ability to translate technical complexity to customer-facing narratives.
  • Comfortable working autonomously and managing multiple deployment tracks in parallel.

Preferred Qualifications

  • Familiarity with CI/CD, infrastructure-as-code (Terraform, Pulumi), and container orchestration (Docker, Kubernetes).
  • Background in LLM fine-tuning, retrieval-augmented generation (RAG), or AI/ML operations.
  • Previous experience in a startup, consulting, or fast-paced customer-obsessed environment.

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