Senior AI Engineer
Magicforce
Overview: Job Title: Senior AI Engineer Location: Remote Duration: 12+ Months Job Description: We are looking for a Senior AI Engineer to design, build, and deploy high-quality AI-powered features with end-to-end ownership from prototyping to production, ensuring reliable, scalable, and impactful AI solutions. Mandatory skills Core AI Skills
- Strong understanding of LLM capabilities and limitations
- Experience with prompt engineering and structured output design
- Hands-on experience with embeddings and vector search
- Familiarity with RAG architectures and when to apply them
- Experience designing agent-based architectures (AgentCore concepts)
- Understanding of tool use, planning strategies, and memory mechanisms in LLM systems
- 4+ years of related work experience
- Solid backend/system design fundamentals
- Experience building and deploying production-grade systems
- Ability to debug complex issues, including probabilistic outputs
- Comfort working with APIs, pipelines, and data flows
- Ability to translate user needs into effective AI solutions
- Strong intuition for balancing quality, latency, and cost
- Focus on delivering measurable product impact
- Communicates clearly across engineering and product teams
- Contributes to team knowledge and shared practices.
- Evaluate agent performance across multi-step tasks (task success rate, error propagation)
- Debug and optimize agent decision-making and tool selection behavior
- Design and implement AI-powered features (LLM workflows, copilots, and agent-based systems with tool use and multi-step reasoning)
- Own the Full Lifecycle: prototyping → evaluation → production deployment → iteration
- Ensure solutions are reliable, performant, and aligned with product needs
- Build and optimize prompt pipelines for specific use cases
- Build retrieval systems (embeddings, chunking, ranking)
- Implement RAG-based workflows where needed
- Iterate on outputs to improve quality, accuracy, and consistency
- Design scalable and cost-efficient AI architectures for production workloads
- Select and evaluate models (hosted vs open-source) based on use case constraints
- Design and build agentic workflows capable of multi-step reasoning and decision-making
- Integrate agents with tools, APIs, and internal systems to perform real-world actions
- Implement planning, execution, and reflection loops for complex tasks
- Manage context, memory, and state across multi-step interactions
- Balance deterministic workflows vs. agent autonomy for reliability and control
- Run structured experiments to compare approaches (prompting, retrieval, models)
- Define and track key metrics for AI performance (quality, latency, cost)
- Debug and improve non-deterministic system behavior
- Build and maintain evaluation datasets and benchmarks
- Implement automated evaluation pipelines for continuous improvement
- Drive technical direction and influence AI adoption across teams
- Partner with product managers and designers to scope AI features
- Contribute to shared patterns and reusable components
- Participate in code reviews and design discussions
- Support and mentor mid-level engineers where needed
- Design guardrails to ensure safe and reliable AI behavior
- Mitigate hallucinations, prompt injection, and model misuse
- Ensure compliance with data privacy and enterprise requirements
- Implement monitoring and observability for AI systems in production
- Implement guardrails for agent actions (tool access control, execution boundaries)
- Prevent failure cascades in multi-step agent
Vacancy posted 1 day ago
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