Senior AI Engineer

Location:
Hybrid (Madrid or Lisbon preferred)

Type:
Full-time

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 assembling a world-class team to match. Our squads are lean, highly skilled, and execution-driven.

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

What You'll Do

As a
Senior AI Engineer (GenAI)
at InteractiveAI, you'll play a key role in developing our next-generation AI capabilities, advancing our LLM, SLM, and fine-tuning workflows while contributing to the core model development that powers our platform.

You'll work closely with the Chief of AI and a cross-functional squad to design, experiment with, and deploy cutting-edge foundation models, agentic architectures, and evaluation frameworks. You will own hands-on experimentation, model training, optimization, and productionization—helping to push the boundaries of GenAI performance inside enterprise environments.

You'll contribute to org-wide AI standards, model development best practices, and high-quality engineering execution.

• Build and maintain scalable pipelines for structured/unstructured data ingestion, transformation, and feature engineering
• Deploy ML models, LLMs, and SLMs into production, ensuring performance, reliability, and traceability
• Develop fine-tuning pipelines for foundation models, with versioned checkpoints, experiment tracking, and evaluation workflows
• Implement automated evaluation frameworks (A/B testing, LLM-as-judge, validation suites) and dashboards tracking latency, accuracy, drift, and maintenance triggers
• Develop feature engineering, imputation, and data transformation strategies for complex, real-world use cases
• Implement and optimize retrieval-augmented generation (RAG) pipelines, vector search, and grounding strategies
• Build enterprise-grade agentic workflows, integrate tools, and evaluate agentic system performance
• Optimize inference speed, memory usage, and cost across LLM/SLM deployments
• Ensure reliability and performance of models in production, addressing issues around latency, accuracy, drift, and scaling
• Collaborate with product and delivery teams to ship measurable, client-ready AI capabilities and accelerate new GenAI features

What We're Looking For

We're looking for a highly skilled AI engineer with strong foundations, hands-on GenAI experience, and a track record of building production-grade AI systems. You should be capable of contributing to core architecture discussions while also executing end-to-end model development work.

Minimum Requirements:

• 4+ years
  in data engineering, ML engineering, applied AI, or related deep technical roles
• Experience deploying ML models and LLMs/SLMs to production, with strong inference optimization skills
• Hands-on experience with agent orchestration tools (LangGraph, LlamaIndex, or similar)
• Experience training deep learning models and fine-tuning LLMs using modern frameworks
• Fluency in Python and experience with at least one deep learning framework (PyTorch preferred; TensorFlow or JAX also acceptable)
• Strong experience building production-grade data pipelines (batch or streaming) using tools like Airflow, Spark, or Dagster
• Solid understanding of ML theory (bias-variance tradeoff, metrics, optimization, evaluation, probability, etc.)
• Comfortable with cloud platforms (AWS, GCP, Azure) and containerized deployments
• Strong communication skills and ability to work effectively in cross-functional teams

Additional Requirements:

• Experience with LLMs/SLMs and RAG pipelines in production
• Familiarity with vector databases, embeddings, and document retrieval strategies
• Exposure to MLOps practices (monitoring, reproducibility, CI/CD for ML, automated evaluation)
• Experience optimizing inference latency, throughput, and cost at scale
• Experience working in regulated or enterprise environments (e.g., banking, insurance)
• Bonus: experience with model distillation, quantization, or training smaller models (SLMs)

Who You Are

Proactive & Resourceful: You anticipate challenges, propose solutions, and help push model performance forward.

High-Ownership Engineer: You move with accountability, take responsibility for outcomes, and consistently raise the bar.

Entrepreneurial & Adaptive: You thrive in ambiguity, operate with speed, and deliver in a high-paced startup setting.

Collaborative Teammate: You work well across disciplines and help foster a culture of high performance.

What You'll Get

• Competitive base salary (€90,000/yr to €110,000/yr)
• performance bonuses
• Access to equity/share plan as it rolls out.
• Health & wellness allowances
• 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:

1. Intro Call
   – 30 minutes to align on fit and expectations
2. Take-Home Challenge
   – A practical task based on real-world problems
3. Technical Interview
   – Deep dive into the challenge, technical experience, and AI engineering
4. Cultural and Values Interview
   – Discussion on motivation, cultural and value alignment
5. 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 —