Software Engineer- AI & Node.js

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

• 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 & 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.
• 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.