Note: The job is a remote job and is open to candidates in USA. AMISEQ is seeking an Applied AI Engineer to identify, architect, and deploy AI solutions for high-value enterprise workflows. The role focuses on building agentic systems, intelligent workflows, knowledge graph integrations, and reliable production AI services while collaborating across teams, mentoring engineers, and driving experimentation and technical innovation.
Responsibilities
- 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
Skills
- 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
- Ability to effectively communicate complex technical topics to senior leadership and non-technical stakeholders
- 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
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