RemoteTechnology

Staff Engineer - Applied AI

Amiseq Inc.United States🇺🇸United States

Why this role stands out

This remote Staff Engineer role offers a unique opportunity to architect and deploy cutting-edge applied AI solutions, driving innovation in enterprise workflows and mentoring fellow engineers. You'll thrive here if you possess a strong background in software engineering and a passion for building scalable, resilient AI systems that solve complex business challenges. This is an excellent chance to significantly impact intelligent automation and grow your expertise in a flexible, remote setting.

Quick overview

SeniorityMid Senior

Work modeRemote

LocationUnited States

SpringJavaLLMGenerative AIMachine LearningRAGSystem Design

Job description

Job Title: Staff Engineer - Applied AI

Location: 100% Remote

Duration: 12+ Months W2 Contract to Hire

Job Description:

• Identify and evaluate opportunities for automating business processes using AI, intelligent workflows, and agent-based systems.

• Architect, build, and deploy applied AI solutions across high-value enterprise workflows including automation, document intelligence, decision support, and intelligent assistants.

• Design and implement AI agents and agentic workflows that orchestrate tools, APIs, reasoning steps, and business logic to automate complex processes at scale.

• Build systems and services that meet high standards for scalability, resilience, performance, and availability.

• Use knowledge graphs to enhance reasoning, entity relationships, context retrieval, and multi-step workflows.

• Collaborate with product, engineering, operations, and analytics partners to co-create scalable AI solutions and translate business needs into technical designs.

• Mentor engineers and scientists who want to develop AI and agentic workflow skills through coaching, pairing, reviews, and architectural guidance.

• Drive innovation by exploring new models, frameworks, and reasoning techniques and applying them creatively to real-world challenges.

• Lead through technical influence by providing guidance on architecture, experimentation, and deployment across multiple teams.

• Run rigorous experimentation and evaluation including hypothesis definition, measurement, validation, and iterative improvement in production environments.

• Establish and model engineering best practices for reliability, interpretability, safety, governance, and monitoring of production AI systems.

What We Are Looking For (Must Have):

• 8 or more years of professional software engineering or applied machine learning experience, including 2 or more years working with Generative AI or LLM-based systems in production.

• Experience adding intelligence to internal processes and workflows to improve efficiency, automation, and decision-making

• Track record of improving system reliability and scalability through architectural improvements, performance optimization, and infrastructure enhancements

• Proven experience building scalable, resilient, secure, and maintainable products and systems that run reliably in production.

• Strong understanding of agent architectures, workflow orchestration, retrieval-augmented generation, vector databases, and knowledge graph integration.

• Ability to collaborate deeply across teams and co-create solutions with engineers, product managers, and domain experts.

• Experience mentoring engineers and helping others grow in AI, LLM, and agent-based system design.

• A history of delivering measurable business outcomes from AI systems.

• Strong competency in distributed systems, service design, performance optimization, and reliability engineering.

Nice to Have:

• Experience building advanced Generative AI capabilities including domain-tuned LLMs, vector reasoning techniques, or specialized retrieval architectures.

• Experience with insurance, financial services, or other regulated industries.

• Experience deploying AI components in Java ecosystems including Spring AI, LangChain4j, or Embabel.

• Background in document intelligence, fraud or anomaly modeling, or complex ontology and knowledge graph design.

• Familiarity with AI safety practices, evaluation frameworks, monitoring, and regulatory compliance.

Ability to effectively communicate complex technical topics to senior leadership and non-technical stakeholders.