Jobs at Obin AI

Build and lead production-grade agentic AI systems for regulated financial institutions. Responsibilities include setting AI governance and evaluation standards, designing LLM and RAG agents, owning backend infrastructure and APIs, optimizing reliability and performance, partnering with customers, shaping platform architecture, and mentoring engineers. The role requires extensive software engineering and AI/ML experience, strong Python and cloud infrastructure skills, and expertise in multi-agent orchestration, safety, and system evaluation.

Build and evaluate LLM-based agentic applications for regulated financial workflows. Responsibilities include developing evaluation methods, optimizing prompts and contexts, conducting applied and exploratory AI research, integrating models and proprietary data, designing AI-native systems, and applying statistics and machine learning to investment processes such as sourcing, screening, due diligence, and asset monitoring.

Build and scale production LLM-powered agents and RAG systems for regulated financial workflows. Responsibilities include designing agent orchestration, backend infrastructure, APIs, evaluation and optimization frameworks, observability, and reusable AI platforms. The role involves direct customer collaboration, technical architecture ownership, and establishing engineering standards. Candidates should bring substantial software engineering experience, production experience with AI agents, Python expertise, cloud infrastructure knowledge, and strong systems architecture and communication skills.

Build and deploy production-grade LLM-powered agents and RAG systems for regulated financial workflows. Responsibilities include designing agent orchestration, owning backend infrastructure and APIs, evaluating accuracy and reliability, optimizing performance and cost, establishing AI safety standards, shaping platform architecture, partnering with customers, and mentoring engineers. The role also contributes to technical strategy, engineering culture, hiring standards, and long-term product direction.

Design enterprise multi-cloud architectures and agentic AI platforms for regulated financial institutions. Build multi-agent workflows, RAG systems, voice agents, streaming AI applications, and vector database routing. Architect Kubernetes-based infrastructure across GCP, AWS, and Azure using Terraform, while supporting Spark and Airflow data pipelines, BigQuery, Snowflake, and governed data ecosystems. Lead technical advocacy, publish reusable architectures, and align research, engineering, and executive stakeholders.

Own full-cycle recruiting for technical, AI/ML, engineering, product, and GTM roles during a 6–12 month engagement. Responsibilities include developing sourcing strategies, mapping talent markets, partnering with hiring leaders, managing candidate processes, maintaining pipeline data, negotiating offers, improving interview processes, and advising on AI/ML hiring trends and compensation. This hands-on individual contributor role requires strong technical recruiting expertise, stakeholder influence, operational rigor, and candidate experience focus.

Own enterprise financial services accounts end to end, including stakeholder relationships, opportunity qualification, ROI validation, deal scoping, pricing, closing, renewals, expansions, QBRs, and delivery handoffs. Navigate regulated risk, legal, procurement, and security reviews while partnering with presales, engineering, product, and delivery teams. The role requires insurance industry knowledge, quota or renewal ownership, technical fluency, and strong executive communication.

Own the executive relationship, commercial quota, pricing, ROI strategy, and expansion roadmap for a flagship insurance client. Advise C-suite stakeholders, lead steering committees, navigate regulatory and procurement reviews, close enterprise deals, and drive 20%+ annual account growth. Manage and develop Client Partners while coordinating commercial, product, presales, and delivery teams. Represent the insurance vertical in product and go-to-market strategy.

The Full-Stack Engineer will build and scale LLM-based applications, owning technical decisions and ensuring deployment in real customer environments while working directly with the founders and clients in a fast-paced setting.

The Data Scientist will implement, evaluate, and improve LLM-based applications, develop innovative evaluation methods, and streamline investment analytics through AI.

Design and deploy LLM-powered agents, work with clients, maintain production infrastructure, optimize systems, and contribute to platform design.

The Technical Project Manager will oversee project lifecycles, manage client relationships, coordinate cross-functionally, and ensure timely delivery of solutions for enterprise clients in the financial services sector.

The role involves owning customer success by translating needs into product workstreams, driving discovery, delivering solutions, and participating in hands-on deployments, focusing on finance and AI integration.