ML / AI Engineer
Tasks:
- Build and continuously evolve shared AI capabilities, reusable services, and engineering accelerators that can be adopted across multiple construction software products.
- Design and implement production-grade AI systems including LLM-based experiences, retrieval pipelines, tool use, orchestration layers, and agentic workflows.
- Create human- and machine-executable technical artifacts such as specs, evals, interface definitions, and workflow contracts that improve clarity, speed, and quality of delivery.
- Define and apply evaluation-driven development loops for AI systems, covering quality, reliability, latency, cost, grounding, and other relevant performance dimensions.
- Partner with product teams to identify high-value cross-product opportunities, support integration of shared capabilities, and recommend patterns without owning product-specific architecture decisions.
- Contribute to AI-native engineering practices inside the team through Spec-Driven Development, AI-assisted development workflows, automated testing, and intelligent engineering agents.
- Apply practical standards and guardrails for AI reliability, observability, governance, security, and responsible production use of AI solutions delivered by the central team.
- Prototype and validate new technical approaches quickly, turning emerging AI capabilities into scalable building blocks with clear business relevance.
- Support and guide other engineers through technical collaboration, peer review, and knowledge sharing in modern AI engineering and experimentation.
- Work closely with AI PM / PO, engineering, and business stakeholders to align implementation choices with customer value, delivery feasibility, and measurable outcomes.
What We're Looking For:
- At least 5+ years of hands-on software engineering experience, including significant work on production-grade AI or data-intensive systems in complex delivery environments.
- Excellent Python programming skills.
- Proficiency in Azure and AWS cloud platforms.
- Hands-on experience with Generative AI technologies and applications.
- Strong practical understanding of LLMs, retrieval-augmented generation, tool use, agents, and their real-world trade-offs, limitations, and failure modes.
- Availability to work between 2:00 PM and 10:00 PM CET is preferred.
- Experience designing and operating end-to-end AI systems across APIs, application services, data pipelines, retrieval layers, evaluation workflows, and cloud infrastructure.
- Ability to define measurable quality criteria and evaluation approaches for AI systems, balancing customer value with reliability, latency, cost, and maintainability.
- Practical experience with AI-assisted engineering, Spec-Driven Development, and modern developer tooling; able to improve how teams work by combining human judgment with automation and agent support.
- Strong architectural judgment with the ability to design reusable patterns and shared capabilities while collaborating effectively with product teams that own their local implementation decisions.
- Demonstrated track record of contributing to cross-functional work from early exploration and prototyping through production rollout and continuous improvement in agile environments.
- Ability to support and influence other engineers, raise technical standards through collaboration, and create clarity in ambiguous and fast-changing AI environments.
- Strong communication and collaboration skills, with the ability to translate technical complexity into decisions and actions for product, engineering, and business stakeholders.
- Adaptive mindset, high learning agility, and genuine openness to continuous change in a field shaped by rapid advances in AI.
- Master's degree in Computer Science, Software Engineering, Artificial Intelligence, or a related technical field; equivalent experience considered for exceptional candidates.
Originally posted on Himalayas