Genios AI
Overview
We are a scrappy, data-driven, customer-focused startup revolutionizing financial operations with AI-driven workflow automation. We are 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. This role emphasizes delivering minimum loveable products powered by AI—building scalable, reliable, and user-friendly systems. You’ll work across model pipelines, infrastructure, and product layers with an emphasis on applied AI delivered in production. This is a full-time role 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.
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 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.)
#J-18808-Ljbffr
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. This role emphasizes delivering minimum loveable products powered by AI—building scalable, reliable, and user-friendly systems. You’ll work across model pipelines, infrastructure, and product layers with an emphasis on applied AI delivered in production. This is a full-time role 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.
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 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.)
#J-18808-Ljbffr