Applied AI Lead

For over 19 years, OLSYS has partnered with private equity firms, startups, and enterprise leaders across finance, healthcare, automotive, and retail to co-create transformative AI, cloud, and software solutions.

We unite a 200+ strong engineering community within an inclusive, high-ownership culture where ambitious talent can elevate their expertise, solve complex challenges, and thrive together.

Join us to deliver technical excellence you take pride in, and let’s shape what’s next together.

About the Client

Our client is North America’s leading network of independent aftermarket truck parts distributors. The customer`s distributors serve the needs of their clients from more than 700 locations across the United States, Canada, Puerto Rico, and Mexico. The customer`s distributors are specialists who understand the demands of their local, regional, and national clients for quality parts and exceptional service.

Our client is a proud member of NEXUS North America and NEXUS Automotive International, a worldwide group of parts distributors committed to bringing a global approach to the automotive and commercial vehicle aftermarket industries.

About the Role

We are looking for a hands-on Applied AI Lead to own the development of a parts recognition system end to end — from defining the technical approach to delivering and continuously improving a production-ready solution.

The goal is to identify industrial parts, such as air springs, from user-provided photos and match them against the company's parts catalog.

This role requires more than choosing technologies or leading implementation. We are looking for someone with a strong technical ownership mindset and a broad, systems-level perspective — someone who can anticipate problems, identify gaps and failure modes early, and recognize when a solution is not working as intended, even when the issue is not immediately obvious.

You will be expected to think several steps ahead, challenge assumptions, and take responsibility for the technical outcome of the product rather than simply executing a predefined solution.

We value reliable, cost-effective results over technology for its own sake.

Requirements:

• 10+ years of professional experience in software engineering, machine learning, or a related technical field.
• Proven experience delivering at least one AI/ML or data-intensive product to real users.
• Strong hands-on experience building production AI solutions, preferably involving computer vision, LLMs, or multimodal AI.
• Strong Python skills and solid software engineering practices.
• Experience with .NET or Java in production environments.
• Practical experience with LLMs and modern AI technologies.
• Strong understanding of machine learning fundamentals, including model training, evaluation, deployment, and monitoring.
• Experience designing reliable data pipelines, APIs, testing, and monitoring.
• Strong focus on data quality, including identifying inconsistencies, missing information, and potential sources of unreliable results.
• A measurement-driven approach: ability to define meaningful metrics, build evaluation datasets, and validate whether a solution actually works.
• Strong systems thinking and the ability to understand how technical decisions affect the entire product, not just an individual component.
• Proven ability to identify risks and failure modes proactively rather than reacting to problems after they reach production.
• Ability to work independently and make sound technical decisions in an environment where requirements, data, and solutions are not always fully defined.
• Proven ability to own projects end to end with limited supervision.
• Strong problem-solving skills, ownership, and a high level of personal responsibility.
• Ability to explain technical concepts and trade-offs clearly to non-technical stakeholders.

Nice to Have:

• Experience working with computer vision or image recognition systems.
• Ability to read and understand engineering drawings or a background in mechanical/industrial engineering.
• Experience with automotive, heavy-vehicle, aftermarket, MRO, or industrial parts catalogs.
• Experience with product data, catalog management, or master data projects.
• Previous experience working in a startup or small team where flexibility and wearing multiple hats were required.

English level: Upper-Intermediate

Responsibilities:

• Take end-to-end ownership of the parts recognition solution, from initial design through production and continuous improvement.
• Develop a deep understanding of the parts, product catalog, available data, and real-world images provided by users.
• Define and evaluate the overall technical approach, considering accuracy, cost, scalability, maintainability, and operational risks.
• Think beyond the immediate implementation: anticipate potential problems, edge cases, bottlenecks, and failure modes before they become production issues.
• Proactively identify when assumptions, data, architecture, or model behavior do not match the intended outcome — and determine what needs to change.
• Challenge proposed approaches when necessary and validate that the system is solving the actual business problem, not just producing technically impressive results.
• Make pragmatic technology choices across AI/ML, computer vision, LLMs, data processing, APIs, and supporting infrastructure.
• Work hands-on with AI/ML technologies and contribute directly to the implementation of the solution.
• Identify and address data gaps using practical approaches such as data enrichment, preprocessing, augmentation, or alternative data sources.
• Define success criteria, evaluation metrics, test datasets, and quality benchmarks.
• Build reliable data pipelines, APIs, and production services supporting the AI solution.
• Establish monitoring and quality controls to detect degradation, unexpected behavior, and data or model issues.
• Analyze real-world results and continuously improve the solution based on data and user feedback.
• Balance technical quality with business constraints, delivery timelines, cost, and long-term maintainability.
• Clearly communicate technical decisions, trade-offs, risks, and results to technical and non-technical stakeholders.
• Take responsibility for the overall technical direction and outcome of the project with a high degree of autonomy.

What success looks like:

• First 3 months: you know the catalog, the data and the problem inside out, have proposed an approach with clear reasoning, and have a way to measure accuracy.
• By 6 months: a working system is in front of real users, with quality controls in place.
• By 12 months: accuracy is measured and improving release over release, and you have a clear, data-backed view of what comes next.