AI engineer
Robotics Technologies LLC
Sunnyvale, TX, United States
about 2 months ago
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Role details
Contract type
Temporary contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
10 years minimum
Working hours
Regular working hours
Job source
Tech stack
Clean Code Principles
Application Programming Interfaces (APIs)
Artificial Intelligence
Amazon Web Services
Microsoft Azure
Continuous Integration
Python (Programming Language)
Cloud Services
Software Engineering
SQL Databases
Pulumi
Google Cloud
+10 more
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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