Mainder
AI/ML Platform Engineer
MainderSpain13 days ago
Full-timeRemote FriendlyOther
AI Platform Engineer

About Our Partner

We are partnering with an innovative technology company focused on advancing AI-powered software solutions through cutting-edge integration of Large Language Models and intelligent systems. They are building sophisticated AI infrastructure designed to bridge the gap between AI research and production-ready applications, with particular focus on scalable model deployment, MLOps pipelines, and enterprise-grade AI services. The company is seeking highly motivated professionals to help scale their AI platform from experimental prototypes to commercial deployment.

Position Overview

We are supporting our partner in their search for an AI Platform Engineer to join their Barcelona office. As the critical bridge between AI research and production software, you will play a key role in building and maintaining the AI infrastructure that enables researchers to deploy their work safely, reproducibly, and at scale. Your role will involve architecting model serving infrastructure, implementing MLOps pipelines, optimizing AI performance, and collaborating closely with AI developers and backend engineers to integrate cutting-edge AI capabilities into production systems.

Your Mission

AI Infrastructure and Model Deployment

  • Build and maintain AI infrastructure including model serving, vector databases, and embedding pipelines
  • Deploy and serve LLMs from multiple providers (OpenAI, Anthropic, HuggingFace, fine-tuned models)
  • Implement vector database solutions (Pinecone, Chroma, Weaviate, FAISS) for efficient retrieval
  • Optimize inference latency, costs, throughput, and reliability across AI services
  • Design and implement caching, rate limiting, and retry strategies for production AI systems
  • Enable AI developers to deploy their work reproducibly and safely at scale

MLOps and Pipeline Development

  • Version models, prompts, datasets, and evaluation results systematically
  • Implement experiment tracking using tools like Weights & Biases or MLflow
  • Build CI/CD pipelines specifically for model deployment and testing
  • Monitor model performance, drift, and system health in production
  • Set up comprehensive logging and observability for AI services
  • Define workflows from notebook/test repository → PR → staging → production
  • Establish best practices for moving from research to production

API Development and Integration

  • Design and implement robust APIs for AI inference using FastAPI
  • Create endpoints for prompt testing, model selection, and evaluation
  • Build APIs for prompt management and experimentation
  • Integrate AI services seamlessly with backend application architecture
  • Ensure API reliability, security, performance, and proper error handling
  • Implement async programming patterns for efficient AI service delivery

Collaboration and Technical Enablement

  • Work closely with AI developers (researchers) to productionize their experiments
  • Collaborate with backend engineers to integrate AI capabilities into the product
  • Define and document workflows for AI development and deployment
  • Review code and mentor AI developers on software engineering best practices
  • Document AI infrastructure, APIs, and operational procedures
  • Enable research teams to move faster from idea to production

Performance Optimization and Reliability

  • Optimize AI inference latency and cost efficiency
  • Implement monitoring and alerting for AI service health
  • Debug complex distributed AI systems and resolve production issues
  • Ensure high availability and fault tolerance of AI services
  • Conduct performance profiling and implement optimization strategies
  • Balance trade-offs between latency, cost, throughput, and model quality

The Impact You Will Have

  • Enable AI researchers to deploy their innovations into production systems safely and efficiently
  • Build the AI platform infrastructure that scales from experiments to enterprise deployment
  • Reduce inference costs and latency while maintaining model quality
  • Establish MLOps standards that support the company's long-term AI strategy
  • Directly influence the reliability, performance, and scalability of AI-powered features
  • Bridge the gap between cutting-edge research and practical production systems

Key Requirements

  • Proven experience (5+ years) in software engineering, preferably with focus on AI/ML systems
  • Strong programming skills in Python with experience in production environments
  • Experience with LLMs and AI/ML in production: OpenAI API, HuggingFace, LangChain, or similar frameworks
  • Understanding of vector databases (Pinecone, Chroma, Weaviate, FAISS) and similarity search
  • Cloud infrastructure experience: GCP (Vertex AI preferred) or AWS (SageMaker)
  • API development expertise: FastAPI, REST, async programming patterns
  • CI/CD and DevOps skills: Docker, Terraform, GitHub Actions
  • Monitoring and observability experience for distributed systems
  • Problem-solving mindset: comfortable debugging complex distributed AI systems
  • Operating experience with AI deployment in enterprise environments
  • Fluent oral and written communication in English (additional European languages are a plus)

Highly Valuable

  • Experience fine-tuning or training machine learning models
  • Familiarity with AI frameworks (LangChain, Pydantic AI, or similar)
  • Knowledge of prompt engineering techniques and evaluation methodologies
  • Experience with real-time inference and streaming responses
  • Background in data engineering or ML engineering roles
  • Understanding of RAG (Retrieval-Augmented Generation) architectures
  • Experience with experiment tracking tools (MLflow, Weights & Biases)
  • Contributions to open-source AI/ML projects
  • Knowledge of Kubernetes for container orchestration
  • Experience with model versioning and A/B testing frameworks
  • Familiarity with cost optimization strategies for LLM deployments

Tech Stack

Languages

  • Python (primary)
  • Bash scripting

AI/ML Frameworks

  • OpenAI API, Anthropic, Gemini
  • HuggingFace Transformers
  • LangChain, Pydantic AI

Vector Databases

  • Pinecone, Chroma, Weaviate, FAISS

Backend & APIs

  • FastAPI, SQLAlchemy, Pydantic

Cloud & Infrastructure

  • Google Cloud Platform (Vertex AI, Cloud Run, Cloud Functions)
  • Terraform (Infrastructure as Code)

MLOps & Monitoring

  • Experiment Tracking: MLflow, Weights & Biases, or custom solutions
  • CI/CD: GitHub Actions, Cloud Build
  • Containers: Docker, Kubernetes (optional)

Who You Are

  • Resilient and open to challenges, with a never-give-up attitude
  • A collaborative team player who bridges technical and research teams effectively
  • Self-motivated, detail-oriented, and systematic problem-solver
  • Proactive and creative in solving issues under uncertainty with a "solutions, not problems" mindset
  • Professional with excellent written and verbal communication skills
  • Able to work independently and handle confidential information
  • Passionate about making AI research accessible and production-ready
  • Dedicated to establishing and maintaining sustainable MLOps processes and best practices
  • Committed to continuous learning in the rapidly evolving AI landscape
  • Comfortable operating at the intersection of research and engineering

What They Offer

  • Highly Competitive Compensation: Top-of-market salary package that reflects your expertise and the value you bring
  • Cutting-Edge Technology: Work with state-of-the-art AI technologies and the latest LLMs from leading providers
  • Work-Life Balance: Flexible work arrangements with options for remote work
  • Professional Growth: Opportunities to attend industry conferences, engage with the AI/ML community, and expand your technical expertise
  • Impact-Driven Culture: Join a passionate team focused on solving challenging problems at the intersection of AI research and production engineering
  • Technical Autonomy: Shape the AI platform architecture and have real influence on infrastructure decisions
  • Learning Environment: Work alongside AI researchers and engineers pushing the boundaries of what's possible

Why Join?

At this company, you will be working on technology that brings cutting-edge AI research into real-world production applications. This is your opportunity to build the AI platform infrastructure that enables researchers to deploy innovative models at scale while maintaining enterprise-grade reliability and performance. You'll see your work directly enable groundbreaking AI capabilities, while collaborating with a talented team of AI developers, backend engineers, and product leaders. If you're ready to take your expertise in AI infrastructure and MLOps to the next level and want to be at the forefront of production AI systems, we want to hear from you!

We are dedicated to creating a diverse, inclusive, and authentic workplace. If this role excites you but your background doesn't perfectly match every qualification, we still encourage you to apply. You could be the perfect fit for this position or another opportunity within our growing team.

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