[Hiring] Senior Backend (AI / Machine Learning) Engineer @Afternow

Role Description

We are looking for a Senior Backend (AI / Machine Learning) Engineer who can design, build, and deploy intelligent systems in real-world production environments.

What You'll Do

• Machine Learning Engineering: Design, implement, and deploy machine learning features in production environments.
• Data Pipelines: Build reliable data pipelines for processing, transforming, and preparing data for ML models, analytics, and intelligent product features.
• API Development: Create internal (gRPC) and external (REST) APIs that allow AI/ML services to be consumed by other applications and systems.
• Experimentation & Optimization: Support experimentation, validation, monitoring, and continuous improvement of ML models and AI-driven features.
• Performance & Scalability: Ensure that ML-powered features are reliable, performant, maintainable, and built to scale.
• Cross-functional Collaboration: Work closely with product managers, backend engineers, frontend engineers, QA, and design teams to define, refine, and deliver intelligent product experiences.
• End-to-end Ownership: Own AI and ML capabilities from technical discovery and prototyping through production deployment, monitoring, maintenance, and iteration.
• Cloud & Infrastructure Collaboration: Work with DevOps and backend teams to deploy, monitor, and scale ML-powered services in AWS cloud environments.
• AI-assisted Development: Use coding agents and AI development tools to improve development speed, testing, documentation, and code quality, while remaining accountable for architecture, security, reliability, and production outcomes.
• AI/ML Service Development: Develop and maintain dedicated AI/ML services, primarily using Python and modern backend engineering practices.
• Model Integration: Integrate, serve, and maintain ML models within scalable backend systems and microservices architectures.
• AI Evaluation & Observability: Define evaluation criteria, test datasets, monitoring, tracing, and quality metrics for AI-powered features. Identify failure modes and implement appropriate safeguards and fallback behaviour.

Qualifications

• 5+ years of backend development experience with Python & preferably Java Spring Boot.
• Strong hands-on experience with Python and common ML/data libraries and frameworks.
• Experience building and deploying production-grade machine learning features, not only experiments or notebooks.
• Solid understanding of data pipelines, model serving, APIs, and backend system integration.
• Experience with MLOps tools and workflows, cloud infrastructure, preferably AWS.
• Familiarity with microservices architecture and modern backend engineering practices in Java.
• Understanding of ML lifecycle concepts such as training, evaluation, deployment, monitoring, and iteration.
• Experience with databases, data processing, and structured/unstructured data.
• Familiarity with NLP, RAG, LLMs, recommendation systems, prediction models, vector databases, embeddings, agentic AI systems, tool calling, workflow orchestration, or other applied AI/ML domains is a strong plus.
• Ability to translate complex data and ML concepts into practical, usable product features.
• Strong problem-solving skills and a pragmatic engineering mindset.
• Hands-on experience using AI coding tools or agents as part of a professional software development workflow.
• Experience evaluating AI or ML systems using automated tests, offline evaluations, production metrics, and human feedback.
• Clear communication skills and the ability to collaborate with technical and non-technical stakeholders.
• Languages: Fluent in English; Spanish-speaking is an advantage.

Requirements

• 4+ years of experience in backend development (Python, Java).
• Hands-on experience in implementing and deploying AI features & Machine Learning models and data pipelines using Python.

Benefits

• Flexibility in working hours while maintaining a healthy work-life balance.
• Fully remote work environment with tools for time tracking and work transparency.
• Culture that values collaboration, openness, and a willingness to learn.
• Autonomy and responsibility in your role.

Hiring Process

• Application review: We’ll review your experience, background, and overall fit for the role.
• Video Call with HR: Initial conversation about fit, availability, and expectations.
• Second interview with Leadership: Focus on cultural fit, ownership, communication, and collaboration.