Applied AI Engineer
Confirmed on the employer's own hiring board on Sep 29, 2026. First seen by Alion on Sep 4, 2026. Salt scores C on the Alion truth index.
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Applied AI Engineer
Ref: JO-2609-363170
Insurance
250 - 999 Employees
Environment:
Remote
Contract Type:
Permanent
Starts:
2026-11-02
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About the Opportunity
We are partnering with a fast-growing technology company developing a new generation of AI-native applications designed to make everyday tasks, communication, organization and workflows more intelligent and intuitive.
The team is building proactive AI experiences with a strong focus on persistent context, reliable long-running workflows and successful real-world task completion.
They are looking for an Applied AI Engineer to bridge the gap between advanced model capabilities and real-world product experiences.
About the Role
As an Applied AI Engineer, you will turn model capabilities into reliable, production-ready product behavior.
You will own problems end-to-end, from shaping model behavior and designing AI workflows through to building the surrounding systems and ensuring they perform effectively in production.
Sitting at the intersection of machine learning, systems engineering and product, this role is focused on making AI work for real users - not simply building models or demonstrations.
What You’ll Be Doing
- Build and ship AI-powered features end-to-end, from model to system to user experience.
- Design, test and continuously improve prompts, tools, memory systems and agent workflows.
- Transform raw model outputs into structured, reliable and predictable product behaviors.
- Build and improve agentic systems capable of interacting with tools, data and external services.
- Debug issues across the complete AI stack, including models, orchestration, infrastructure and user experience.
- Optimize AI systems for latency, cost, performance and production reliability.
- Develop lightweight evaluation frameworks to measure real-world model and system performance.
- Identify model and system failure modes and develop mechanisms to improve reliability.
- Work closely with product, engineering and research teams to translate ambiguous problems into working solutions.
- Continuously iterate on models and systems using production data, user behavior and measurable performance signals.
What We’re Looking For
- Strong foundation in machine learning and modern neural network architectures.
- Hands-on experience training, fine-tuning and/or deploying machine learning models.
- Practical experience working with Large Language Models (LLMs) and modern generative AI systems.
- Experience building AI-powered applications or features that have been deployed into production.
- Ability to write clean, maintainable and production-quality code.
- Comfortable working across multiple abstraction layers, from models and infrastructure through to product.
- Understanding of inference, model serving and production ML considerations.
- Strong problem-solving skills and the ability to navigate ambiguous technical challenges.
- Ability to make pragmatic engineering decisions in a fast-moving environment.
- A strong bias toward shipping, experimentation, iteration and continuous improvement.
Technology Environment
Experience across some of the following technologies and areas would be highly relevant:
- Python
- PyTorch
- JAX
- Large Language Models (LLMs)
- Commercial and open-source model APIs
- LLaMA
- Qwen
- vLLM or similar inference/serving technologies
- Vector databases
- Model training and fine-tuning
- AI agents and tool calling
- Memory and context systems
- Model evaluation frameworks
- Production ML infrastructure
What Success Looks Like
AI and machine learning capabilities will successfully translate into reliable, production-ready product experiences that meet defined accuracy, latency and reliability targets.
Production issues will be quickly identified, debugged and resolved at their root cause, whether they originate within the model, orchestration layer, infrastructure or product experience.
Data pipelines, training processes and inference systems will remain robust, reproducible and maintainable as the product scales.
You will work closely with engineering, product and research teams to ensure improvements are driven by real-world signals and measurable outcomes, continuously increasing the performance and reliability of AI-powered features.
Salt is acting as an Employment Agency in relation to this vacancy.
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