Software Engineer-AI at Moodys
New York, NY 10007, USA -
Full Time


Start Date

Immediate

Expiry Date

08 Dec, 25

Salary

156200.0

Posted On

09 Sep, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Azure, Operational Efficiency, Ecs, Computer Science, Research, Mongodb, Docker, Software Development, Reliability, Python, Kubernetes, Databases, Node.Js

Industry

Computer Software/Engineering

Description

At Moody’s, we unite the brightest minds to turn today’s risks into tomorrow’s opportunities. We do this by striving to create an inclusive environment where everyone feels welcome to be who they are—with the freedom to exchange ideas, think innovatively, and listen to each other and customers in meaningful ways.
If you are excited about this opportunity but do not meet every single requirement, please apply! You still may be a great fit for this role or other open roles. We are seeking candidates who model our values: invest in every relationship, lead with curiosity, champion diverse perspectives, turn inputs into actions, and uphold trust through integrity.

SKILLS AND COMPETENCIES

  • 3+ years of experience in backend software development with a focus on Node.js, building scalable and production-grade systems required.
  • Hands-on experience with AI applications, including LLM implementations, retrieval-augmented generation, prompt optimization, and fine-tuning methodologies.
  • Demonstrated proficiency in artificial intelligence concepts, with hands-on experience using AI tools to streamline workflows and enhance operational efficiency.
  • Proven ability to implement AI-powered solutions to solve business challenges.
  • Demonstrates a growing awareness of AI risk management and a commitment to responsible and ethical AI use.
  • 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).
  • Familiarity with Python for collaborating on machine learning workflows.

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.
  • Mentor junior engineers and collaborate across disciplines to drive impactful solutions while aligning system design with business outcomes.
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