Lead AI Engineer
hace 7 días
Madrid
ppInteractiveAI is a fast-growing startup on a mission to empower enterprises with fully managed AI agent lifecycles. /p pWe are building the next generation of enterprise AI solutions, delivering an innovative, scalable, and compliant agentic AI infrastructure to businesses across industries. Our platform allows organisations to build, own, and scale AI that learns from every interaction. /p pWe value autonomy, speed, and innovation and are 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. /p h3What You’ll Do /h3 pAs a bLead AI Engineer /b at InteractiveAI, you’ll operate as the Chief of AI’s right hand, driving the technical direction of our AI stack, accelerating new use cases, and guiding the development of advanced GenAI capabilities across the platform. /p pYou’ll take ownership of the design, development, and deployment of cutting‑edge models, agentic architectures, and fine‑tuning workflows. You will lead experimentation efforts, influence architecture decisions, mentor engineers, and ensure our AI systems are scalable, reliable, and aligned with enterprise requirements. /p pYou’ll work in a cross‑functional squad while also contributing to org‑wide AI standards, frameworks, and best practices. /p ul liBuild and maintain scalable pipelines for structured/unstructured data ingestion, transformation, and feature engineering /li liLead the deployment of ML models and LLMs into production, ensuring performance, reliability, and traceability /li liArchitect and oversee fine‑tuning pipelines for LLMs with versioned checkpoints, evaluation suites, and experiment tracking /li liDesign and implement automated evaluation frameworks (A/B testing, LLM‑as‑judge, validation suites) and monitoring dashboards to track latency, accuracy, drift, and trigger retraining or alerts /li liGuide feature engineering, imputation, and transformation strategies in complex, real‑world scenarios /li liImplement and optimize retrieval‑augmented generation (RAG) workflows, vector search approaches, and knowledge‑grounding strategies /li liLead the development of enterprise‑grade agentic workflows, tooling integrations, and agent evaluation methods /li liOptimize inference speed, memory usage, and cost for high‑throughput systems across the platform /li liOwn reliability and performance of models in production, solving challenges around latency, accuracy, drift, and scaling /li liCollaborate with product and delivery teams to ship client‑ready, measurable outcomes and accelerate new AI‑driven features /li /ul h3What We’re Looking For /h3 pWe’re looking for a btop‑tier AI engineer /b with strong foundations, proven delivery, and the leadership abilities required to build production‑ready, enterprise‑grade AI systems. You should be equally capable of executing hands‑on and guiding the strategic evolution of our platform. /p ul li5+ years in data engineering, ML engineering, applied AI, or similar deep technical roles /li liExperience deploying ML models and LLMs to production at scale, with strong inference optimisation skills /li liHands‑on experience with agent orchestration tools (LangGraph, LlamaIndex, or similar) /li liExperience training deep‑learning models and fine‑tuning LLMs using modern frameworks /li liFluent in Python and experienced with at least one major deep learning library (PyTorch, TensorFlow, JAX, etc.) /li liStrong experience building production‑grade data pipelines (batch or streaming) using tools like Airflow, Spark, Dagster /li liSolid understanding of ML theory (bias‑variance tradeoff, probability, metrics, optimisation, evaluation, etc.) /li liComfortable with cloud platforms (AWS, GCP, Azure) and containerised deployments /li liExcellent communication skills with proven ability to mentor engineers or lead technical workstreams /li /ul h3Additional Requirements /h3 ul liExperience with LLMs and RAG pipelines in production environments /li liFamiliarity with vector databases, embeddings, and document retrieval strategies /li liExposure to MLOps practices: model monitoring, reproducibility, CI/CD for ML, automated evaluations /li liExperience optimizing inference latency, throughput, and cost at scale /li liExperience working in regulated or enterprise environments (e.g. banking, insurance) /li liBonus: prior experience in technical leadership roles, architecture ownership, or acting as a technical right‑hand to a CTO/Chief of AI /li /ul h3What You’ll Get /h3 ul liCompetitive base salary (from €110,000/yr to €130,000/yr) + performance bonuses /li liAccess to equity/share plan as it rolls out. /li liHealth wellness allowances /li liPrivate health insurance /li liFlexible work setup + travel when needed (ideally Hybrid in Lisbon or Madrid) /li li25 days of holidays/paid time off (excluding local public holidays) /li /ul h3Who You Are /h3 ul liProactive Vision‑Driven: You anticipate challenges, propose solutions, and help shape the future of our AI stack. /li liHigh‑Ownership Leader: You move with accountability, take responsibility for outcomes, and raise the engineering bar. /li liEntrepreneurial Adaptive: You thrive in ambiguity, operate with speed, and deliver in a high‑paced startup setting. /li liCollaborative Mentor: You work across disciplines, guide others, and contribute to a culture of high performance. /li /ul h3Interview Process /h3 pWe keep our process focused and respectful of your time. Most candidates complete it in 2–3 weeks. Here’s what to expect: /p ul liIntro Call – 30 minutes with our team to align on fit and expectations /li liLive Coding Interview Challenge – A practical task based on real‑world problems /li liCultural and Values Interview – Discussion on motivation, cultural and value alignment /li liOffer – Final conversation and offer /li /ul pWe’re building a team of builders — people who care about impact, quality, and growth. If that’s you, let’s talk — /p /p #J-18808-Ljbffr