Senior AI Engineer /Tech Lead

Job Purpose:

The Senior AI / LLM Engineer & Tech Lead is the technical backbone of the AI engineering team, responsible for turning clearly defined business problems into reliable, scalable, production-grade AI solutions that are actively used by the business.

This role sets the technical direction and quality bar for how AI systems are designed, built, deployed, and operated—moving decisively beyond experiments and prototypes to systems that meet real-world requirements and scale.

Working in close partnership with the AI Solutions Lead, data scientists, and data engineers, the role ensures that decisions across models, data, application architecture, and infrastructure form a coherent and production-ready whole. The role also plays a key mentoring and technical leadership function, shaping engineering standards, frameworks, and best practices across the team.

Differentiator:

• Typically a highly experienced manager who combines deep technical expertise with strong product and business intuition.

• Balances hands-on system building with technical leadership, ensuring that solutions are not only well-designed but also practical, usable, and impactful.

Key Tasks:

• Design and build AI-powered workflows and agent-based systems, including LLM orchestration, RAG pipelines, tool use, and multi-step automations across freight commercial and operational use cases.
• Translate problem briefs into end-to-end technical designs, defining appropriate models, data architectures, infrastructure choices, and key engineering trade-offs.
• Apply strong engineering judgment to balance speed, scalability, and maintainability — avoiding unnecessary complexity and over-engineering.
• Make clear, informed build-versus-buy decisions, selecting frameworks and tooling pragmatically and avoiding unnecessary complexity or vendor lock-in.

• Own the full journey from early prototype to robust production deployment, ensuring solutions scale reliably under real operational constraints.
• Maintain a strong bias toward shipping — prioritizing working systems in the hands of users over perfect but unused solutions.
• Ensure that what works in development continues to work in production — managing latency, cost, reliability, observability, and failure modes from day one.
• Establish patterns and standards that ensure what works in experimentation continues to work at scale.

• Co-design data pipelines, feature engineering, and data representations in close partnership with data scientists and data engineers.
• Act as an equal technical collaborator rather than a downstream consumer, shaping how data is structured and served to enable high-quality AI outcomes.

• Set and reinforce engineering standards, architectural patterns, and best practices for AI and LLM-based systems.
• Mentor and technically guide data scientists and junior engineers, raising the overall quality, robustness, and maintainability of solutions across the team.
• Lead by example through hands-on building, code reviews, and architectural decision-making grounded in real-world constraints. Lead by example through hands-on building, code reviews, and architectural decision-making grounded in real-world constraints.

• Partner closely with the AI Solutions Lead to align technical approaches with business intent and delivery priorities.
• Engage selectively with business stakeholders where technical feasibility, constraints, or opportunities need to be understood early.
• Act as a credible technical voice, able to explain trade-offs and constraints in a clear and pragmatic way.

Stakeholders:

• Work closely with the AI Solutions Lead and stakeholders to ensure technical solutions align with real business needs, challenging underspecified or impractical problem statements early.
• Prototype alongside discovery, using early builds and experiments to refine both the solution and the problem definition.
• Continuously incorporate user feedback and usage patterns into system design, ensuring solutions are not only functional but adopted.

Management responsibility:

• Manage a department or a small unit that includes multiple teams led by Managers and/or Team Leaders

Skills:

Large Language Models (LLMs) Prompt engineering LLM orchestration Agent systems Retrieval-Augmented Generation (RAG) Tool calling / function calling Multi-step reasoning workflows Model evaluation Failure-mode analysis Python TypeScript Backend engineering API design Microservices Event-driven architectures Production system design Debugging at scale

Strong engineering judgment, pragmatic decision-making, and the ability to balance speed, quality, and cost in real-world system

Qualifications & Key Requirements

Education level

Software engineering, machine learning engineering, or applied AI roles. Meaningful experience taking AI or ML systems from concept through production and sustained operation in a real business environment.

Experience level

More than 8 years tems that are actively used by business users, with a clear understanding of the gap between prototypes and production systems.