Klick
Life Sciences
AppliedAIDeveloper
Neural analysis suggests this role is
optimal for mid candidates.
“Applied AI Developer at Klick. Skills: Applied AI, LLM Integration, Agentic Workflows, Backend Development. Build AI-backed tools. Maintain AI-backed tools”
What You'll Achieve.
Tools people use; Measured by usage; Measured by outcomes
Industry & Context.
Root cause analysis
What They're Looking For.
Must Have
3+ years backend applications Python, Integrate LLM APIs, Ship multi-step AI workflows, Evaluate AI output quality, Design modular systems, Explain technical decisions
Nice to Have
Using AI as work tool, Multiple LLM providers experience, Deploy applications Google Cloud, Deploy applications AWS, Portfolio AI projects, Healthcare domain knowledge, Pharma domain knowledge
What You'll Do.
Build AI-backed tools
Maintain AI-backed tools
Design agentic workflows
Coordinate agent teams
Handle asynchronous jobs
Build human review checkpoints
Match approach to problem
Justify model choices
Turn ideas into systems
Communicate technical decisions
How You'll Work.
Team & Collaboration
Internal teams; External audiences
Communication Scope
Write clearly; Present work
Process & Methodology
Scoping, Implementation, Deployment, Iteration
Full Job Description
The Klick Group— comprising Klick Health (including Klick Katalyst and btwelve), Klick Media Group, Klick Applied Sciences (including Klick Labs), Klick Consulting, and Klick Ventures—is an ecosystem dedicated to realizing the full potential of our people and clients in life sciences. Founded in 1997, we have offices in New York, Philadelphia, Saratoga Springs, Toronto, London, São Paulo, and Singapore. Consistently named a Best Managed Company, Most Admired Corporate Culture, and Great Place to Work, Klick is committed to fostering a high-performance, people-first culture with creativity, collaboration, innovation, and impact across everything we do. We are hiring an Applied AI Developer to build production systems powered by Large Language Models. You will design and ship tools that automate internal workflows and support client-facing work. The role is backend-heavy: you will write Python, build agentic operations that chain model outputs together, and build the infrastructure around them. This is not an ML role. No model training, no fine-tuning, no weights. You work with existing generative AI services and the tools they expose. What You'll Do: * Build and maintain AI-backed tools. Take a problem through scoping, implementation, deployment, and ongoing iteration. Projects vary in scale from single-purpose automations to larger pipelines that support multiple teams. * Own deployed work. Tools that people actually use, measured by real usage and outcomes. You care what happens after launch, not just whether it ran once. * Design agentic workflows. Chain LLM calls into pipelines rather than relying on one-shot prompts. Coordinate agent teams, handle asynchronous jobs, and build checkpoints for human review at the right points. * Plan for failure modes. AI systems need guardrails: validation against known data, human review gates, continuous monitoring, and fallbacks when models produce bad output. You think about reliability from the start, not as a patch after th
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