Ai Engineer Jobs

About The Position

Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field (or equivalent practical experience)
• 2+ years of software development experience building and deploying AI/ML applications
• Hands-on experience building LLM-based applications
• Designing multi-step LLM workflows and task-specific agents
• Experience working with frontier models (e.g., OpenAI, Anthropic, Google)
• Experience with AI tools as a user, specifically AI code editors
• Developing advanced prompt engineering strategies, evaluation frameworks, and RAG pipelines
• Conducting technical R&D to explore and define the boundaries of model functionality
• Use of evaluation tools such as Langfuse or LangSmith
• Strong backend engineering experience
• Building APIs from the ground up using Python frameworks such as FastAPI and Django
• Deploying and scaling containerized applications in cloud environments (e.g., AWS, GCP, Azure)
• Ability to design and maintain scalable, production-grade backend systems for AI applications
• Ability to create, orchestrate, and evaluate LLM-based agents and chained workflows with minimal oversight
• Ability to implement and orchestrate multi-step agentic workflows
• Ability to debug and improve LLM-driven systems, identifying issues across multiple layers (model output, API behavior, system logic)
• Ability to conduct rapid experimentation and research on LLM capabilities and translate findings into production functionality
• Ability to stay current with emerging practices, models, and tooling in the generative AI ecosystem and apply them pragmatically
• Ability to communicate clearly with technical and non-technical collaborators (e.g., product managers, medical writers, customer teams)
• Ability to operate effectively in a fast-paced, ambiguity-heavy environment, managing shifting priorities and novel problem spaces

Nice To Haves

• Worked with Infrastructure-as-Code tools such as Terraform or Pulumi
• Implementing CI/CD pipelines (e.g., GitHub Actions )
• Experience working in or adjacent to regulated domains (life sciences, clinical R&D) is a plus
• Frontend development experience (e.g., React ) is a plus, but not required

Responsibilities

• Developing, scaling, and expanding the Artos platform to serve regulatory needs for pharma and life science companies around the globe.
• Building and deploying AI/ML applications.
• Building LLM-based applications.
• Designing multi-step LLM workflows and task-specific agents.
• Developing advanced prompt engineering strategies, evaluation frameworks, and RAG pipelines.
• Conducting technical R&D to explore and define the boundaries of model functionality.
• Building APIs from the ground up using Python frameworks such as FastAPI and Django.
• Deploying and scaling containerized applications in cloud environments (e.g., AWS, GCP, Azure).
• Design and maintain scalable, production-grade backend systems for AI applications.
• Create, orchestrate, and evaluate LLM-based agents and chained workflows with minimal oversight.
• Implement and orchestrate multi-step agentic workflows.
• Debug and improve LLM-driven systems, identifying issues across multiple layers (model output, API behavior, system logic).
• Conduct rapid experimentation and research on LLM capabilities and translate findings into production functionality.
• Stay current with emerging practices, models, and tooling in the generative AI ecosystem and apply them pragmatically.
• Communicate clearly with technical and non-technical collaborators (e.g., product managers, medical writers, customer teams).
• Operate effectively in a fast-paced, ambiguity-heavy environment, managing shifting priorities and novel problem spaces.