EMPLOYMENT
Full-time
COMPENSATION
$180k - 280k/yr
ARRANGEMENT
On-site
At a glance
Own production AI systems end to end, building LLM applications, RAG pipelines, agentic workflows, and evaluation infrastructure that solve complex life sciences problems.
Summarized by AI from the original posting
What you'll do
- Build and optimize production LLM applications, agentic workflows, and RAG pipelines
- Experiment with models, prompting, retrieval strategies, and architectures
- Build evaluation frameworks to measure output quality
- Work with proprietary datasets for enterprise AI workflows
- Design and build agentic systems for complex, multi-step tasks
- Take AI systems from experimentation through deployment, monitoring, and continuous optimization
- Build infrastructure and testing for reliable production AI systems
- Contribute across backend, infrastructure, and product to ship complete solutions
Requirements
- 01Strong software engineering fundamentals
- 02Experience shipping production systems
- 03Hands-on experience building applications with modern LLMs
- 04Experience with RAG, agentic workflows, prompt engineering, or LLM evaluation
- 05Strong Python engineering skills
- 06Experience with modern backend and cloud infrastructure
- 07Understanding of testing, monitoring, and improving AI systems in production
- 08Strong communication and collaboration skills
Perks
Competitive compensationMeaningful equityLong-term career progression
Skills
PythonLlmsRAGRetrievalAgentic WorkflowsPrompt EngineeringLlm EvaluationAi Evaluation FrameworksBackend InfrastructureCloud InfrastructureMonitoringTesting
Full description
Role: Applied AI Engineer
Space: Agentic AI x Life Sciences
Location: New York City (Onsite)
Interested in building production AI systems where the quality of the underlying models directly impacts the product?
We're partnered with a fast-growing, venture-backed AI company building complex enterprise software within life sciences.
They're looking for an Applied AI Engineer to join an early technical team and own AI systems from experimentation through to production, working across LLMs, RAG, agents, evaluations, and the infrastructure needed to make them reliable at scale.
This is a highly hands-on role where you'll experiment quickly, ship into production, measure what works, and continuously improve systems using real customer feedback.
What You'll Be Doing:
• Build and optimize production LLM applications, agentic workflows, and RAG pipelines
• Experiment across models, prompting, retrieval strategies, and architectures to improve performance
• Build evaluation frameworks to measure output quality and turn feedback into measurable improvements
• Work with complex proprietary datasets to power high-quality enterprise AI workflows
• Design and build agentic systems capable of handling complex, multi-step tasks
• Take AI systems from experimentation through deployment, monitoring, and continuous optimization
• Build the infrastructure and testing needed to make AI systems reliable in production
• Flex across backend, infrastructure, and product when needed to ship complete solutions
What They're Looking For:
• Strong software engineering fundamentals with experience shipping production systems
• Hands-on experience building applications with modern LLMs
• Experience across RAG, agentic workflows, prompt engineering, or LLM evaluation
• Strong Python engineering skills and experience with modern backend and cloud infrastructure
• Understanding of how to test, monitor, and improve AI systems in production
• Comfortable experimenting rapidly while maintaining strong engineering standards
• Strong communicator who enjoys working closely with engineering, product, and customers
Why Join?
• Build agentic AI systems that are already solving real enterprise problems
• Own the full AI lifecycle from experimentation and evaluation through to production
• Work on challenging problems across LLMs, RAG, agents, and AI infrastructure
• Have significant influence over technical direction within an early engineering team
• Work directly with customers and use real-world feedback to improve the systems you build
• Competitive compensation, meaningful equity, and strong long-term career progression
If you're interested in learning more, I'd be happy to share additional details.