AI Software Engineer

As a Software Engineer specializing in AI systems, you will play a key role in designing and implementing production‑grade software solutions that leverage cutting‑edge machine learning techniques, including large language models (LLMs), natural language processing systems (NLP), and AI agents. Your primary focus will be building scalable, efficient, and maintainable backend systems using Node.js and Python, while also integrating machine learning workflows and AI‑driven applications. You will work closely with data scientists, machine learning engineers, and other stakeholders to develop robust platforms capable of supporting advanced AI capabilities.

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.