Description
Robots & Pencils is seeking an AI Engineer to design, build, and deploy production-grade AI systems that deliver measurable business value.
This is a hands-on engineering role focused on implementation and delivery. You will contribute to scalable AI systems and ML pipelines, working within established architectural direction while collaborating closely with senior engineers, product managers, and cross-functional teams.
You will be expected to deliver independently on well-defined features, contribute to system design discussions, and maintain high standards for reliability, performance, and maintainability.
Key Responsibilities
AI System Implementation
- Implement machine learning models, inference services, and supporting APIs
- Contribute to the development of training, evaluation, and deployment workflows
- Build data ingestion and feature engineering components aligned to defined architecture
- Ensure reliability, performance, and stability of AI services in production environments
AI Pipelines & Infrastructure
- Contribute to the development and orchestration of AI pipelines and agentic workflows.
- Support LLMOps processes for automated model deployment, versioning, and prompt management.
- Implement monitoring and observability to track token usage, model drift, and hallucinations.
- Debug and resolve complex issues across LLM layers, data ingestion, and cloud infrastructure.
Research-to-Production Support
- Collaborate with senior engineers to operationalize research prototypes
- Implement evaluation metrics and validation processes
- Support rollout and iteration of AI features based on performance data
Collaboration & Delivery
- Work closely with product and engineering teams to implement AI-driven features
- Participate in sprint planning, estimation, and technical discussions
- Contribute to documentation and shared engineering standards
- Communicate progress, blockers, and risks clearly
Required Skills & Qualifications
- 3–5 years of experience in AI/ML engineering or applied machine learning
- Experience building and deploying ML models in production environments
- Strong software engineering background (Python or similar)
- Familiarity with distributed systems and scalable data processing
- Experience contributing to ML pipelines and automated deployment workflows
- Working knowledge of MLOps concepts (model versioning, monitoring, CI/CD)