AI Engineer (Software & AI Applications)

We are hiring an AI Engineer to design and operate production-grade AI/ML systems that directly improve product and operational outcomes.

Our current focus is building a robust AI Agent Platform that enables automation of complex workflows with strong control, validation and continuous learning.

This role requires a strong engineer with a general production system mindset who can build reliable systems in production. You will play a key role in building our agent platform, which requires strong backend coding skills. The ability to build frontend components and work as a full-stack engineer is highly preferred and considered a strong advantage.

Depending on priorities, you may also work on:

• Search and retrieval systems (keyword, semantic, hybrid)
• Recommendation, ranking and personalization systems
• AI evaluation, monitoring and reliability systems
• Other AI-powered product or operational systems with measurable business impact

What You Will Build & Responsibilities

• Design and build end-to-end AI/ML systems from problem definition to production rollout and iteration.
• Architect and build the core components of the AI Agent Platform to support complex, multi-step workflows and autonomous task execution.
• Develop backend services and APIs that expose AI capabilities safely and reliably.
• Apply AI processing techniques, including data processing and data labeling, to prepare high-quality datasets for model training.
• Train and deploy machine learning models, ranging from recommendation models to fine-tuning LLMs for specific business and operational purposes.
• Develop and evolve AI Agent workflows, including context handling, validation and fallback logic.
• Build retrieval, search, ranking and personalization components that connect user intent to the right data or action.
• Integrate LLMs with structured and unstructured data via context construction and tool-enabled workflows.
• Build monitoring, evaluation and human-in-the-loop workflows for continuous improvement.

Requirements

Must-have:

• Strong software engineering foundation with a general production mindset and strong Python skills.
• Deep understanding of AI and ML models, with hands-on experience in AI data processing and data labeling.
• Proven experience in training machine learning models (e.g., recommendation models) and fine-tuning LLMs for practical applications.
• Experience in AI Engineering or ML Engineering.
• Experience building backend services and APIs.
• Hands-on experience building LLM-based applications.
• Understanding of RAG, embeddings, vector databases, retrieval and search pipelines.
• Experience in at least one of the following: AI agent or multi-step AI workflows; Search, retrieval or relevance systems; Recommendation, ranking or personalization systems.

Nice to Have:

• Full-stack development capabilities or experience building frontend interfaces for AI products.
• Experience with large-scale search or recommendation systems.
• Experience with agent orchestration frameworks or tool-using LLM systems.
• Experience with A/B testing, experimentation or evaluation pipelines.
• Experience with observability, monitoring and production reliability.
• Experience with multimodal systems (voice, OCR, image).
• Experience in high-scale operational domains (fintech, payments, customer operations).
• Experience mentoring or leading projects.

How You Work

• You are an engineer first: you care about clean code, maintainability and production reliability.
• You have an AI mindset: you understand when to use rules, ML, LLMs or hybrid approaches.
• You have an applied mindset: you start from the problem and choose the simplest effective solution.
• You are proactive: you propose, build, test and improve continuously.

Success Metrics

Impact is measured by real outcomes in operations and product performance:

• Reduce manual workload through automation of repetitive workflows.
• Increase automation rate for eligible tasks.
• Reduce system errors and escalation to human handling.
• Improve output quality and overall user satisfaction.
• Increase key product metrics such as CTR, conversion and payment rate.
• Improve user retention and engagement.
• Ensure system stability and scalability as traffic and complexity grow.
• Maintain strong observability, control and continuous improvement via monitoring and feedback loops.

What Success Looks Like

• Systems you build are deployed and used in production at scale.
• Clear and measurable impact on business and product metrics.
• High system reliability with strong quality control and guardrails.
• Continuous improvement driven by evaluation and feedback.

Why Join us

• Work on end-to-end AI systems, not isolated models.
• Solve real-world problems at scale.
• High ownership with visible impact on product and business metrics.
• Opportunity to shape the architecture of AI platforms (Agents, Search, Recommendation).