Software Engineer, Applied AI at Abs Group in Knoxville, TN | USD120,000-150,000 | Kablio

Role

Description

ai standards

ai systems

rapid prototyping

performance monitoring

full-stack ai

* Shape best practices**: Contribute to internal standards for applied AI development, evaluation, and deployment at scale.
Build at the frontier**: Design and implement end-to-end AI systems, including multi-agent workflows, retrieval pipelines, and customized LLMs.
* Prototype rapidly**: Transform research concepts into production-grade systems through fast iteration, disciplined testing, and continuous refinement.
* Raise the bar on evaluation**: Develop rigorous truth sets, automated quality checks, and real-time monitoring pipelines to quantify performance and business outcomes.
* Engineer full-stack solutions**: Deliver web and backend applications that seamlessly integrate AI, ensuring reliability, scalability, and strong user experience.

Requirements

mlops

rag

full-stack

llm

langchain

bachelor’s

* Familiarity with MLOps practices: CI/CD, model versioning, monitoring, and deployment at scale
* Deep knowledge of RAG architectures, embeddings, vector databases, and retrieval optimization strategies
* Full-stack engineering skills across modern web and backend technologies
* Strong grounding in applied information retrieval and vector-based systems
* Experience designing evaluation frameworks and truth sets for applied AI quality assurance
* Bachelor’s degree in computer science, Engineering, or a related field
* Proven track record delivering AI agents, RAG pipelines, or fine-tuned models with measurable business impact
* Experience with LLM fine-tuning, prompt design, and model performance evaluation
Strong expertise in agent frameworks and LLM orchestration (API-first development, Vercel AI SDK, LangChain, etc.)
* 5+ years of software development experience, including 3+ years building production-grade AI systems

Benefits

Information not given or found

Training + Development

Information not given or found

Interview process

Information not given or found

Visa Sponsorship

Information not given or found

Security clearance

Information not given or found

Company

Overview

1971 Founded

Year Established

The company was established in 1971 as a technical advisory arm of a marine classification society.

.originally focused on marine and offshore services, it expanded across industrial manufacturing, oil, gas, chemicals, energy, government, life sciences and LNG sectors.
headquartered in Spring, Texas, the firm operates globally, delivering data-driven solutions to secure critical assets and operations.
typical projects include process safety management, integrity assurance, asset reliability programs, certification services and digital risk analytics.
their work spans high-stakes industries—from offshore vessel inspections to LNG facility maintenance and governmental risk management.
with heritage rooted in marine classification since 1971, they uniquely blend classification-grade rigor with modern analytics.
they’re notable for pioneering maritime cybersecurity, AI-enhanced inspections and software solutions like fleet management platforms.

Culture + Values

Environment + Sustainability

Emissions Data

GHG emissions by scope

Breakdown of greenhouse gas emissions across scopes 1, 2, and 3, adhering to the GHG Protocol Corporate Standard.

Commits to climate‑science-aligned emissions reductions and supports clients, governments and partners on long-term sustainability.
Provides AI-driven CCUS and alternative fuels advisory, with a net‑zero target by 2050 aligned with the Paris Agreement.
Offers data-driven quantitative risk assessments, sustainability strategy and CCUS/AF planning toward carbon neutrality.

Inclusion & Diversity

Articles

Services

Industries

Careers

Role

Description

ai standards

ai systems

rapid prototyping

performance monitoring

full-stack ai

* Shape best practices**: Contribute to internal standards for applied AI development, evaluation, and deployment at scale.
Build at the frontier**: Design and implement end-to-end AI systems, including multi-agent workflows, retrieval pipelines, and customized LLMs.
* Prototype rapidly**: Transform research concepts into production-grade systems through fast iteration, disciplined testing, and continuous refinement.
* Raise the bar on evaluation**: Develop rigorous truth sets, automated quality checks, and real-time monitoring pipelines to quantify performance and business outcomes.
* Engineer full-stack solutions**: Deliver web and backend applications that seamlessly integrate AI, ensuring reliability, scalability, and strong user experience.

Requirements

mlops

rag

full-stack

llm

langchain

bachelor’s

* Familiarity with MLOps practices: CI/CD, model versioning, monitoring, and deployment at scale
* Deep knowledge of RAG architectures, embeddings, vector databases, and retrieval optimization strategies
* Full-stack engineering skills across modern web and backend technologies
* Strong grounding in applied information retrieval and vector-based systems
* Experience designing evaluation frameworks and truth sets for applied AI quality assurance
* Bachelor’s degree in computer science, Engineering, or a related field
* Proven track record delivering AI agents, RAG pipelines, or fine-tuned models with measurable business impact
* Experience with LLM fine-tuning, prompt design, and model performance evaluation
Strong expertise in agent frameworks and LLM orchestration (API-first development, Vercel AI SDK, LangChain, etc.)
* 5+ years of software development experience, including 3+ years building production-grade AI systems

Benefits

Information not given or found

Training + Development

Information not given or found

Interview process

Information not given or found

Visa Sponsorship

Information not given or found

Security clearance

Information not given or found