AI Software Engineer

Skills And Competencies

• 3+ years of experience in backend software development with a focus on Node.js, building scalable and production-grade systems.
• Hands-on experience with AI applications, including LLM implementations, retrieval-augmented generation, prompt optimization, and fine-tuning methodologies.
• Proven ability to optimize systems for latency, cost, and reliability, and to take AI agents from research to production.
• Strong knowledge of cloud platforms (e.g., AWS, GCP, Azure), containerization technologies (e.g., Docker, ECS, Kubernetes), and MLOps practices.
• Proficiency in databases (e.g., PostgreSQL, MongoDB) and caching systems (e.g., Redis, Memcached) for scalable data storage and retrieval.
• Familiarity with Python for collaborating on machine learning workflows and integrating Python-based AI tools is preferred.
• Excellent problem-solving skills, with the ability to navigate ambiguity and deliver impactful solutions aligned with business goals.
• Effective communication and collaboration skills, with demonstrated experience working across cross-functional teams.

Education

• Bachelor’s degree or higher in Computer Science, Software Engineering, or a related field.

Responsibilities

• Design and implement AI-driven backend systems using Node.js, creating APIs and services to support applications such as NLP, search, recommendation systems, and AI agents.
• Build and integrate large language model (LLM) applications and AI agents using techniques such as retrieval-augmented generation, prompt optimization, fine-tuning, and reinforcement learning.
• Develop end-to-end pipelines for data ingestion, feature engineering, model inference (batch and real-time), and integration of AI-driven workflows into production systems.
• Collaborate with data scientists and machine learning engineers to ensure seamless integration of machine learning practices in Gen AI.
• Optimize backend systems for latency, scalability, and cost, applying caching, load balancing, and other performance techniques to support high-volume inference workloads.
• Advocate for and implement MLOps best practices, including monitoring, logging, tracing, automated retraining, and model/prompt versioning to ensure robust and reliable AI systems.
• Build reusable platforms or frameworks that streamline the deployment and monitoring of AI agents and machine learning models.
• Lead the implementation of autonomous agents capable of multi-step reasoning, decision-making, and tool use in production environments.
• Participate in design reviews, write high-quality code, and contribute to documentation to ensure team-wide efficiency and maintainability.
• Mentor junior engineers and collaborate across disciplines to drive impactful solutions while aligning system design with business outcomes.

About The Team

Our Innovation team is responsible for building internal and external solutions leveraging cutting-edge machine learning technology, including LLMs, AI Agents and NLP. The group is made up of a diverse set of professionals spanning a wide range of experience levels. We’re organized into agile squads, each focused on building and scaling innovative AI-driven products that enhance productivity and deliver measurable value. Collaboration is a cornerstone of how we operate. Engineers and data scientists work closely together to experiment, iterate, and deploy solutions in a fast-paced environment. Joining our team means contributing to an open and supportive culture where learning and knowledge-sharing are encouraged, and where every team member has the opportunity to make a tangible impact.