Genios AI

Build and scale AI/ML and GenAI pipelines, deploy and manage GenAI solutions, operationalize LLMs, and deliver production-grade AI features across the stack. | 3+ years in ML/DL pipelines and applied AI, proficiency in Python or Go with AI frameworks, experience shipping production AI features, and strong software engineering background. | About Us We intend to defy the cliches while living them. Change the world working for a scrappy, data-driven, and customer-focused startup that's revolutionizing financial workflows using AI. You will be instrumental in shaping this cutting edge AI product owning features from design to launch. Change the world working for a scrappy, data-driven, and customer-focused startup that's revolutionizing financial operations with AI-driven workflow automation. We’re looking for an Applied AI Engineer who is first and foremost a strong software engineer, with solid foundations in building production systems and a strong applied AI focus. You will be responsible for building functionalities into the product directly and writing production-grade code that brings advanced AI workflows into real-world use. As an Applied AI Engineer, you’ll focus on making minimum loveable products powered by AI—building scalable, reliable, and user-friendly systems. You’ll work across model pipelines, infrastructure, and product layers, but with a clear emphasis on applied AI delivered in production. This role requires ownership, hands-on coding, and the ability to move fast while balancing robustness and innovation. We have full-time roles available with remote work flexibility, hybrid, or on-site based on your preferences and seniority. Note: This remote position requires monthly travel to the Bay area or Seattle for in person team meetings. Key Responsibilities • Build and scale AI/ML and GenAI pipelines from experimental workflows to production-ready systems. • Integrate model training, evaluation, deployment, and monitoring into product workflows • Deploy and manage GenAI solutions such as chatbots, RAG applications, and predictive analytics tools. • Operationalize LLMs and AI agents, including prompt orchestration, chaining, and fine-tuning. • Benchmark models, develop evaluation frameworks, and improve reliability and auditability. • Implement observability, monitoring, and rollback mechanisms to ensure secure, scalable deployments. • Work across the stack—from backend systems to product SDKs—to deliver AI features directly into user-facing applications. • Prototype rapidly, gather feedback, and iterate while keeping scale and maintainability in mind. • Own critical product components and take responsibility for delivering robust, production-grade features. • Collaborate cross-functionally with data scientists, product managers, and engineers to scope specifications and solve real customer problems. • Debug complex issues and perform root cause analysis across model pipelines, infrastructure, and product layers to ensure reliability and continuous improvement. Qualifications • BS or MS in Computer Science, Statistics, or Mathematics, or equivalent experience. • Strong software engineering background with proven experience shipping production systems. • 3+ years of experience in ML/DL pipelines, deployment, and applied AI solutions. • Proficiency in Python or Go with frameworks like TensorFlow, PyTorch, Scikit-Learn, FastAPI, or gRPC. • Experience with LLM and AI frameworks such as Langchain, LlamaIndex, Hugging Face Transformers, and OpenAI API. • Knowledge of RAG architectures, embeddings, reranking models, and LLM-based dialogue systems. • Experience building and scaling backend platforms, APIs, and microservices. • Comfortable working full-stack, from model APIs down to user-facing integrations. • Have shipped AI features that users actually use; production experience over theoretical knowledge. • Track record of building reliable products with strong attention to detail and usability. • Autonomous and excited about taking ownership over major initiatives. • Frequent user of AI products (Cursor, Claude Code, Copilot, etc.) during the development lifecycle. Bonus Points • Production experience with LLMs (APIs or custom implementations) at meaningful scale. • Experience building agentic systems or LLM-enabled products. • Familiarity with prompt tuning methodologies and frameworks like self-prompting, DSPy, or Banks. • Experience with performance optimization and high-scale document indexing systems. • Prior startup experience, grit, and ability to thrive in fast-moving environments. Familiarity with databases and comfortable writing SQL queries. Perks • Competitive compensation • Unlimited PTO • AI Assistants for work (Coding, General Purpose, etc.)