A Typical Day
Build, test, and optimize production-ready LLM agents leveraging multi-step reasoning, prompt engineering, retrieval, memory, and tool-calling capabilities.
Develop scalable evaluation pipelines (Evals) to continuously measure accuracy, task completion, latency, cost efficiency, safety, and hallucination rates.
Integrate agentic workflows with external platforms, vector databases, microservices, MCP, and internal APIs to enable reliable context retrieval and automated action-taking.
Fine-tune domain-specific LLMs using modern adaptation techniques like supervised fine-tuning (SFT), LoRA/PEFT, and preference optimization to maximize performance.
Productionize AI services across cloud and backend architecture, implementing security guardrails, observability, and robust deployment pipelines.
Track production metrics to identify data drift, manage feedback loops, implement retuning strategies, and systematically upgrade model and prompt performance.
Partner with product, data, and engineering teams to prototype emerging AI tech, navigate technical tradeoffs, and scale early-stage concepts into enterprise-grade software.
Bring Your Passion, Do What You Love. Here’s What We’re Looking For
Bachelor’s degree in a relevant field with 5+ years of related experience, an advanced degree with 3+ years of experience, or equivalent practical work experience.
5+ years of professional software/ML/AI engineering experience with demonstrated ownership of production systems. Proficiency in Python is required, alongside experience with backend languages (such as C#, Java, TypeScript) to build robust APIs, microservices, and data integrations.
Hands-on experience developing modern AI patterns, including LLM applications, retrieval-augmented generation (RAG), agentic workflows, multi-step reasoning, and model/tool integration.
Proven track record building and operating AI/ML systems in production, including model evaluation frameworks (Evals), monitoring, safety guardrails, continuous retuning, and observability.
Experience deploying and scaling cloud-native infrastructure, containers, and automated pipelines. Familiarity with Docker, Kubernetes, and MLOps practices is highly valued.
Ability and willingness to work across the full stack—bridging AI models, backend services, cloud infrastructure, data pipelines, and application interfaces.
Strong sense of ownership, technical curiosity, and comfort navigating ambiguity to establish best practices in an evolving AI landscape.
This position requires fluent written and oral communication in English.
Health & Wellness
Hybrid Work Opportunities
Flexible Time Off
Career Development & Mentoring Programs
Health & Wellness Benefits, including competitive health insurance offerings and generous paid parental leave for eligible new parents
Community Volunteering & Company Philanthropy Programs
Employee Peer Recognition Programs – “You Earned it”
Click here to find out more about the benefits we offer.
Our Culture & Commitment
We’re proud to foster a supportive, inclusive environment where career growth, collaboration, and wellness are prioritized. And our benefits go beyond healthcare—offering resources for physical, mental, and professional well-being. Click here to find out more about the benefits we offer. Q2 employees are encouraged to give back through volunteer work and nonprofit support through our Spark Program (see more). We believe in making an impact—in the industry and in the community.
We are an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, genetic information, or veteran status.
