Deep Python engineering experience with a proven track record of shipping production systems.
Significant expertise in applied ML, LLM systems, NLP, retrieval, and agentic architectures.
Strong system-design skills with a focus on tradeoffs in accuracy, latency, cost, and reliability.
Proficient in building evaluation pipelines and metrics for model monitoring.
Ability to navigate complex projects independently in ambiguous environments.
Strong technical judgment differentiating robust AI techniques from less viable options.
Clear communication skills for mentoring and collaboration.
PhD in a relevant technical field or equivalent industry experience.
Responsibilities
Lead the design and implementation of AI/ML systems for financial services workflows.
Architect agentic systems integrating LLMs, retrieval, embeddings, and more.
Develop evaluation infrastructure to measure agent quality and performance metrics.
Translate customer needs into auditable and scalable AI system designs.
Own major AI/ML workstreams from exploration to deployment.
Collaborate with backend, product, and solutions teams for seamless integration.
Mentor engineers in AI system design and productionization.
Establish standards for AI reliability and maintainability.
Benefits
Opportunity to lead cutting-edge AI/ML projects that shape the future of financial automation.
A collaborative environment where mentorship and skill development are encouraged.
Chance to influence product architecture and direction at an early-stage company.
Engagement with complex and high-stakes problem-solving in the financial sector.
Full Job Description
About the Role
Artian AI is looking for a Senior Software Engineer - AI/ML to lead development of core AI systems for our agentic automation platform. You will help design, build, and scale the AI architecture that enables autonomous agents to perform complex, high-stakes work for financial services customers.
This is a senior individual contributor role for someone who can operate across research-informed prototyping, production engineering, evaluation, and system design. You will be expected to make high-leverage technical decisions, mentor other engineers, and drive AI/ML projects from ambiguous problem statements to reliable product capabilities.
What You'll Do
Lead design and implementation of AI/ML systems that power autonomous financial-services workflows.
Architect agentic systems involving LLMs, retrieval, embeddings, tool use, planning, classification, ranking, and structured reasoning.
Build rigorous evaluation infrastructure for measuring agent quality, task completion, hallucination risk, accuracy, latency, and regressions.
Translate customer and product needs into technical AI system designs that are reliable, auditable, and scalable.
Own major AI/ML workstreams from exploration through production deployment.
Partner closely with backend, product, and solutions teams to ensure AI capabilities are deeply integrated into the platform.
Mentor engineers on AI system design, experimentation, evaluation, and productionization.
Establish engineering standards for AI reliability, observability, safety, and maintainability.
What We're Looking For
Deep Python engineering experience and a track record of shipping production systems.
Significant experience with applied ML, LLM systems, NLP, retrieval, embeddings, ranking, or agentic architectures.
Strong system-design skills and the ability to reason about tradeoffs across accuracy, latency, cost, reliability, and maintainability.
Experience building evaluation pipelines, offline and online metrics, model monitoring, or data feedback loops.
Ability to operate independently in ambiguous environments and drive complex projects to completion.
Strong technical judgment and the ability to distinguish promising AI techniques from fragile demos.
Clear communication and mentorship skills.
PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent industry experience.
Nice to Have
Experience building production LLM applications, AI agents, RAG systems, or enterprise automation platforms.
Experience with financial services, compliance, model risk management, auditability, or regulated enterprise workflows.
Experience designing human-in-the-loop workflows, approval systems, or agent control mechanisms.
Experience at an early-stage company where senior engineers are expected to shape product and architecture.
