Machine Learning Engineer

Location: Hybrid (Lisbon or Madrid preferred)

About InteractiveAI

InteractiveAI is a fast-growing startup on a mission to empower enterprises with fully managed AI agent lifecycles.

We are building the next generation of enterprise-AI solutions, delivering an end-to-end Agentic IDE alongside an extensible ecosystem of agentic resources and solutions. Our platform allows companies to orchestrate, monitor, evaluate, deploy and improve AI agents—and soon fine-tune and own their own models.

We value autonomy, speed, and innovation, and we're building a world-class team to match. Our squads are lean, focused, and execution-driven.

If you thrive in high-performance environments and want to be part of a company that rewards transformational outcomes, this is for you.

What You'll Do

As a
Machine Learning Engineer
at InteractiveAI, you'll design, fine-tune, train, and productionize models that power our agentic platform. Embedded in a cross-functional squad, you'll build resilient data and model pipelines, evaluate model quality with rigorous offline/online methods, and ship performant inference services at scale. You'll collaborate closely with product and delivery to turn business problems into measurable ML solutions.

• Build and maintain scalable pipelines for structured/unstructured data ingestion, transformation, and feature engineering
• Train, evaluate, and iterate on ML models (including LLM fine-tuning where relevant) with strong experiment tracking and reproducibility
• Deploy ML models and LLMs into production, ensuring performance, reliability, observability, and traceability
• Implement automated evaluation (A/B tests, LLM-as-judge, validation suites) and dashboards to monitor latency, accuracy, drift, and trigger retraining or alerts
• Apply feature engineering, imputation, and transformation techniques in practical, production scenarios
• Contribute to retrieval-augmented generation (RAG) workflows and measure retrieval and generation quality
• Integrate enterprise-grade agentic workflows and perform systematic evaluation of LLM outputs
• Optimize inference speed and memory usage in high-throughput systems; profile and reduce cost without sacrificing quality
• Monitor and improve model performance in production (latency, accuracy, drift, data quality) with feedback loops
• Work alongside product and delivery leads to ensure client-ready, measurable outcomes

What We're Looking For

We're looking for someone with strong foundations, proven delivery, and the ability to build production-ready ML systems. Here's what success looks like for this role:

1/ Minimum Requirements:

• 3+ years in data engineering, ML engineering, or applied AI roles
• Experience deploying models to production and optimizing inference performance
• Hands-on experience with at least one agent orchestration tool (e.g., LangGraph, LlamaIndex)
• Experience training deep-learning models and fine-tuning LLMs
• Fluent in Python for data and ML development and hands-on experience with at least one deep learning framework (PyTorch, TensorFlow, etc.)
• Experience building data pipelines (batch or streaming) using tools like Airflow, Spark
• Solid grasp of ML concepts (bias–variance tradeoff, supervised vs. unsupervised learning, precision–recall tradeoffs)
• Comfortable working with cloud platforms (AWS, GCP, or Azure)
• Strong communication skills and experience working in cross-functional teams

2/ Additional Requirements:

• Experience with LLMs and RAG pipelines in production
• Familiarity with vector databases, embeddings, and document retrieval strategies
• Exposure to MLOps practices: monitoring, reproducibility, CI/CD for ML
• Experience optimizing inference latency and cost at scale
• Experience working in regulated or enterprise environments (e.g., banking, insurance)

Who You Are

Proactive & Resourceful:
You take initiative to identify gaps and drive solutions without waiting for instructions.

Accountable & High-Ownership:
You treat our codebase and infrastructure as your own, and you honor commitments.

Entrepreneurial Mindset:
You thrive in ambiguity, embrace rapid change, and deliver in a high-paced startup setting.

Team Player:
You collaborate effectively across disciplines, give and receive feedback constructively, and mentor others.

What You'll Get

• Competitive base salary (from €60,000/yr to €120,000/yr) + performance bonuses
• Future equity opportunity for high performers
• Private health insurance
• Flexible work setup + travel when needed (ideally Hybrid in Lisbon or Madrid)
• 25 days of holidays/paid time off (excluding local public holidays)

Interview Process

We keep our process focused and respectful of your time. Most candidates complete it in 2–3 weeks. Here's what to expect:

• Intro Call
  – 30 minutes with our team to align on fit and expectations
• Take-Home Challenge
  – A practical task based on real-world problems
• Technical Interview
  – Deep dive into the challenge, technical experience, and ML engineering
• Cultural and Values Interview
  – Discussion on motivation, cultural and value alignment
• Offer
  – Final conversation and offer

We're building a team of builders — people who care about impact, quality, and growth. If that's you, let's talk —