We’re looking for an experienced Backend Engineer with a Gen AI orientation to join our research team.
We are building a new AI Platform from scratch to standardize AI development methods, ensure excellence and efficiency, and deliver robust, scalable, and compliant AI capabilities. This platform will handle GenAI models deployment, prompt management, data filtering, logging, and monitoring, centrally, so individuals can focus on their own business-specific logic.
Responsibilities:
Build and maintain CI/CD pipelines that support fast, reliable integration and deployment of GenAI systems across complex environments, including orchestrating multiple models/agents and MCP clients/servers.
Develop, deploy and integrate AI solutions and agentic frameworks, creating both commandline tools and interactive notebooks that support our team’s research and workflow automation needs.
Build and manage data pipelines and databases (SQL) for largescale data handling, automating data collection and curation (e.g., adversarial prompts for model red-teaming) to support AI training and evaluation.
Integrate research into production by implementing the latest generative AI advancements (including whitebox LLM development), integrating different models and agents, and developing evaluation frameworks (e.g., agentic reasoning tests) as part of our automated workflows.
Deploy GenAI models from repositories such as HuggingFace onto our AWS platform, connect them to data pipelines, and automate E2E processes for prompt generation, labeling, and response evaluation.
Requirements:
B.Sc. in Computer Science, Computer Engineering, or a related field
5+ years of experience delivering large scale, high performance systems on AWS, with an emphasis on orchestrating data pipelines and AI workflows in production environments.
Expertise in scripting and automation using Python and shell; proven ability to write clean IaC and CI/CD pipelines.
Strong cross functional communication and collaboration skills, with a DevOps mindset to drive best practices across teams.
Hands on experience deploying and integrating generative AI models (e.g., large language models or other AI/ML models) in production.
Hands on experience deploying MCP clients/servers and integrating them into automated AI workflows.
