Artificial Intelligence Engineer

Amiseq Inc.; United States; On-site; USD 150,000 - 230,000; Full time

Job summary

Amiseq Inc. is seeking a senior AI software engineer to design and deploy applied AI solutions across enterprise workflows, including automation, document intelligence, and intelligent assistants. You will architect AI agents, orchestrate tools and APIs, and drive scalable, reliable production systems.

You will mentor engineers, collaborate with product and analytics teams, and explore new models and reasoning techniques to deliver measurable business outcomes in production environments.

Qualifications

• 8+ years of professional software engineering or applied ML experience, including 2+ years with Generative AI or LLMs 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 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.
• History of delivering measurable business outcomes from AI systems.
• Strong competency in distributed systems, service design, performance optimization, and reliability engineering.

Responsibilities

• Architect, build, and deploy applied AI solutions across high-value enterprise workflows.
• Design and implement AI agents and agentic workflows that orchestrate tools, APIs, reasoning steps, and business logic.
• Lead through technical influence on architecture, experimentation, and deployment across teams.
• Run rigorous experimentation with hypothesis definition, measurement, and iterative improvement in production.
• Establish engineering best practices for reliability, interpretability, safety, governance, and monitoring of production AI systems.
• Mentor engineers and scientists to develop AI and agentic workflow skills.
• Collaborate with product, engineering, operations, and analytics to translate business needs into designs.
• Identify opportunities to automate business processes using AI and intelligent workflows.

Skills

Generative AI

LLM production

Distributed systems

Agent architectures

Workflow orchestration

Knowledge graphs

RAG concepts

Performance optimization

Scalability

Team collaboration

Leadership

Tools

LangChain4j

Embabel

Spring AI

Vector databases

Job description

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.