Senior AI Engineer

Technology alone does not create impact- the right teams do. Founded in 2018, Parser is a boutique technology services and consulting firm helping global organisations solve complex business challenges through digital transformation, product development and AI enablement.

We are a fast-growing team of 340+ engineers and consultants across Europe (UK, Spain, Portugal), the Americas (US, Argentina, Uruguay, Colombia), and the Middle East.

We combine global reach with a mindset focused on agility, senior expertise, and close collaboration. We work as an extension of our clients' teams, helping them define the right problems, shape solutions, and deliver technology-driven outcomes that create measurable business value. Our expertise spans software engineering, AI & data, product development, and customer experience, delivered by teams that combine strong technical depth with a consulting mindset.

If you are looking for a place where you can think beyond execution, take true ownership of outcomes, influence decisions, and continuously learn alongside top-tier specialists in a truly global environment, we'd love to meet you.

We are looking for technically strong, pragmatic engineers with a primary focus on AI, including hands- on experience with Generative AI and large language models (LLMs).

Candidates should also bring solid software engineering practices and a good data background, enabling them to build, deploy, and operate AI-driven solutions in production. We value engineers who collaborate openly, adapt quickly, and focus on delivering practical, usable outcomes.

• Collaboration & Ownership with Alignment
  • Share work early and often, making it visible through docs, demos, and incremental PRs
  • Own deliverables following team architecture and workflows
  • Communicate decisions, assumptions, and trade-offs clearly to the team
  • Avoid working in isolation on critical paths; seek alignment when decisions impact others
• Curiosity & Bias to Action
  • Experiment, validate quickly, and iterate based on frequent stakeholder and team feedback
  • Suggest improvements grounded in problem-solving rather than tech preference
  • Balance exploration with delivery, avoiding over-engineering early
• Pragmatism & User-Centric Thinking
  • Optimize for Analytics and Trading adoption, clarity, and trust
  • Make sensible trade-offs to deliver usable value early, even if the solution isn't yet "perfect"
• Adaptability
  • Open to feedback and able to adapt as the team and project scale in communication, scope, and technical direction

• Data & Analytics Engineering
  • Strong SQL and experience working with large analytical datasets
  • Familiarity with distributed data platforms (e.g., Spark, Trino, Databricks)
  • Understanding of data modelling, joins, aggregations, and performance trade-offs
• MLOps & AI Platform Operations
  • Experience operationalizing ML and LLM-based systems in production environments
  • Familiarity with model lifecycle management (training, versioning, deployment, rollback)
  • Understanding of monitoring and observability for ML systems (performance, drift, data quality)
  • Experience with automation around pipelines, evaluations, and deployments
  • Awareness of scalability, reliability, and cost considerations for AI workloads
  • Hands-on experience with AWS, including designing and operating production-grade cloud infrastructure
• Evaluation, Reliability & Safety
  • Understanding of AI evaluation approaches (offline tests, benchmarks, qualitative review)
  • Familiarity with logging, monitoring, and debugging AI-driven systems
  • Awareness of common AI risks (hallucinations, bias, drift) and mitigation strategies
• Software Engineering Practices
  • Strong coding fundamentals and testing discipline
  • Experience with CI/CD pipelines and production environments
  • Comfortable working with evolving requirements and iterative delivery

• Demonstrated +5 years of experience in data engineering and AI/system development in a production environment
• Provenabilitytoshipiterative,user-focusedsolutionswithattentiontoreliabilityandsafety
• Strongcommunicationskillsandacollaborativemindset

• Experienceinanalyticsortradingdomains
• Experience with vector databases, retrieval-augmented generation (RAG), or LM-based tooling in production
• Hands-onexperiencewithAWS,includingdesigningandoperatingproduction-gradecloudinfrastructure

Madrid, Spain. Hybrid (3 days on-site - Chamartín)