COMPANY A1
AI Engineering
AppliedAIEngineer
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Applied AI Engineer at COMPANY A1. Skills: Machine Learning, Systems Engineering, Product Development. Build and ship AI features end-to-end. Design and iterate on prompts”
What You'll Achieve.
ML models in production meet expected accuracy, latency, and reliability targets; Production issues are identified quickly, debugged effectively, and root causes addressed; Data pipelines, training loops, and inference systems are robust, reproducible, and maintainable; Collaborates effectively with engineers, product, and research teams to deliver reliable ML-powered features; Iterations on models and systems are driven by real-world signals and measurable improvements
Industry & Context.
problem-solving skills in ambiguous, fast-moving environments; Debug issues across the full stack; Production issues are identified quickly, debugged effectively, and root causes addressed
What They're Looking For.
Must Have
foundation in machine learning, modern neural network architectures, Hands-on experience with training, fine-tuning, or deploying ML models, Ability to write clean, production-quality code, Comfort working across abstraction layers, problem-solving skills in ambiguous, fast-moving environments, Bias toward shipping, iteration, and continuous improvement
What You'll Do.
Build and ship AI features end-to-end
Design and iterate on prompts
Turn raw model outputs into structured behaviors
Debug issues across the full stack
Develop lightweight evaluation frameworks
Work closely with product and engineering
How You'll Work.
Team & Collaboration
Work closely with product and engineering; Collaborates effectively with engineers, product, and research teams
Full Job Description
COMPANY A1 is building a proactive AI smart assistant for everyday users to bring intelligence to conversations, errands, organising and workflows. Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior. ROLE As an Applied AI Engineer, you will turn model capabilities into real product behavior. You will own problems end-to-end, from shaping model behavior, to building the systems around it, to ensuring it performs reliably in production. This role sits at the intersection of machine learning, systems, and product, focusing on making AI actually work for users, not just in demos, but in real-world usage. FOCUS - Build and ship AI features end-to-end (model → system → user experience) - Design and iterate on prompts, tools, memory, and agent workflows - Turn raw model outputs into structured, reliable, and predictable behaviors - Debug issues across the full stack (model, orchestration, infra, UX) - Optimize for latency, cost, and production reliability - Develop lightweight evaluation frameworks to measure real-world performance - Work closely with product and engineering to translate ambiguous problems into working systems TECH STACK - Python - PyTorch / JAX - LLMs (OpenAI-style APIs, LLaMA, Qwen, etc.) - Inference / serving (e.g. vLLM) - Vector DB IDEAL EXPERIENCE - Strong foundation in machine learning and modern neural network architectures. - Hands-on experience with training, fine-tuning, or deploying ML models - Ability to write clean, production-quality code - Comfort working across abstraction layers (model → infra → product) - Strong problem-solving skills in ambiguous, fast-moving environments - Bias toward shipping, iteration, and continuous improvement OUTCOMES - ML models in production meet expected accuracy, latency, and reliability tar
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