NineTwoThree AI Studio
ML Engineer (Applied AI)
Remote Machine Learning Engineering role with clear candidate location fit.
PostedJul 1, 2026
Eligible countries1 accepted country
Seniority signalMiddle
Work settingRemote
Accepted candidate locations
Spain
Role overview
ML Engineer (Applied AI)
Requirements and responsibilities
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Technology Stack
- Core Frameworks & Arch: Transformer models, modern LLM APIs (Anthropic Claude, OpenAI, AWS Bedrock, etc.), Open-Source LLMs.
- Orchestration & Agentic Design: Experience designing LLM workflows, agentic systems, or retrieval pipelines using frameworks such as Langchain, LangGraph, LlamaIndex, or equivalent approaches.
- Data & Search: Vector databases (Pinecone, pgvector, Milvus, Qdrant, etc.), SQL, and data engineering pipelines.
- Traditional ML: Supervised and Unsupervised learning (Classification, Regression, Anomaly Detection).
- Cloud & Infrastructure: AWS (Lambda, SageMaker, Bedrock, EC2) and modern DevOps/retraining pipelines.
- Languages: Production-grade Python.
Responsibilities
- Architect & Build AI Features: Design and implement robust classical ML and generative AI solutions, striking the right balance between autonomous agentic architectures and deterministic pipelines.
- Evaluate: Design and maintain evaluation frameworks to measure AI quality, reliability, safety, and business impact before and after deployment.
- Integrate & Deploy: Partner closely with full-stack developers and DevOps to seamlessly integrate AI capabilities into client web and mobile applications using serverless architecture (e.g., AWS Lambda) or API endpoints.
- Optimize for Production: Refine prompts, system instructions, and chunking strategies to balance accuracy, latency, token consumption, and data privacy.
- Traditional Predictive Analytics: Clean and process unstructured or historical client data to train/fine-tune custom algorithms for specific business problems (such as forecasting, classification, or anomaly detection).
- Collaborate & Communicate: Actively participate in client discovery sessions, translate ambiguous business requirements into viable technical scopes, and demo prototypes directly to stakeholder teams.
- Maintain Engineering Excellence: Engage in constructive code reviews, implement rigorous validation patterns to test AI outputs, and contribute templates or runbooks to our internal AI knowledge base.
Technical Experience
- Proven Track Record: 3+ years of experience engineering software with a strong focus on machine learning and natural language processing.
- LLM & Generative AI Mastery: In-depth understanding of modern LLM architectures, context window mechanics, semantic search techniques, and the limitations of generative systems. Ability to identify when a deterministic solution is preferable to an LLM or agent-based solution.
- Production experience: Experience building and operating production AI systems, including monitoring, evaluation, debugging, and iterative improvement.
- Evaluation experience: Understanding of evaluation methodologies for LLM-based systems, including retrieval quality, hallucination detection, and task-specific performance measurement. Ability to reason about tradeoffs between quality, latency, cost, reliability, and engineering complexity.
- Python & SQL Proficiency: Exceptional Python coding skills and the ability to query, clean, and structure data efficiently.
- Cloud Infrastructure: Hands-on experience deploying ML or API services within cloud ecosystems, preferably AWS.
- Ownership: Comfortable taking ownership of ambiguous problems from initial discovery through production deployment and ongoing support.
Product & Team Capabilities
- Ambiguity to Execution: Ability to drop into a completely new industry vertical, understand its data constraints, and spin up a working proof-of-concept within a few weeks.
- The "Product Engineer" Mindset: Passion for seeing things ship and understanding why something is being built from a business value standpoint, not just what is being built.
- Communication: Fluent written and spoken English. Comfortable interacting with client stakeholders and breaking down technical workflows into clear concepts.
- Adaptability: Eagerness to experiment with and evaluate fast-emerging AI development tools, models, and frameworks.
- Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field (or equivalent practical experience).
What We Offer
- Annual paid vacation: 20 days off per year during the first 3 years, increasing to 25 days in later years
- Paid sick leave, 10 national holidays, and 2 company days off
- Well-being budget
- Maternity/paternity leave
- Reimbursement of expenses for professional development courses and certifications (up to 100% in agreement with Manager)
- Hardware upon business needs
- Strong positive engineering culture, a tightly-knit team of professionals with a good sense of humor
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