Software Engineer - AI

Position: Staff Software Engineer - AI
hackajob is partnering directly with Moody's Corporation to hire for this role. At Moody's, we unite the brightest minds to turn todays risks into tomorrows opportunities. We do this by striving to create an inclusive environment where everyone feels welcome to be who they arewith the freedom to exchange ideas, think innovatively, and listen to each other and customers in meaningful ways.

Moodys is transforming how the world sees risk. As a global leader in ratings and integrated risk assessment, were advancing AI to move from insight to action enabling intelligence that not only understands complexity but responds to it. We decode risk to unlock opportunity, helping our clients navigate uncertainty with clarity, speed, and confidence. 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 8 years of experience in software engineering, with deep hands-on experience designing, coding, testing, and operating scalable, resilient, production-grade backend systems and cloud-native services Expert-level coding capability in modern programming languages such as Python, Type Script, Java, Go, or similar, with the ability to personally contribute high-quality production code while guiding technical direction Deep hands-on expertise building enterprise AI applications using large language models, AI agents, retrieval-augmented generation, prompt engineering, orchestration frameworks, evaluation methods, and model optimisation techniques Proven ability to take complex AI solutions from prototype to production, making practical engineering trade-offs across performance, scalability, reliability, security, maintainability, and cost Expert knowledge of cloud platforms such as Amazon Web Services, Google Cloud Platform, or Microsoft Azure, with strong experience using Docker, Kubernetes, Elastic Container Service, or equivalent technologies in production environments Strong experience designing and implementing application programming interfaces, distributed systems, event-driven architectures, data pipelines, PostgreSQL, MongoDB, Redis, vector databases, observability, and automated deployment pipelines Demonstrated ability to influence technical direction while remaining close to the codebase, mentoring engineers through design reviews, code reviews, pairing, debugging, and hands-on problem solving Deep expertise in artificial intelligence, with a track record of implementing advanced AI solutions to drive strategic transformation, platform scalability, and operational efficiency Demonstrated commitment to responsible AI practices, including AI risk awareness, ethical use, governance, evaluation, monitoring, and continuous improvement of AI-enabled products and services Education Bachelors degree or higher in Computer Science, Software Engineering, Artificial Intelligence, or a related technical field, or equivalent practical experience Responsibilities Design, code, and lead delivery of scalable AI platforms and intelligent applications that bring emerging AI capabilities into production Act as a hands-on technical leader, spending significant time designing, coding, reviewing, debugging, and improving production systems that support AI-powered products and services Design and build scalable backend services, application programming interfaces, data pipelines, inference pipelines, and platform capabilities that support real-time and batch AI workloads at enterprise scale Implement advanced large language model applications using retrieval-augmented generation, prompt orchestration, evaluation frameworks, model optimisation, agentic workflows, and tool integration Make key technical decisions while remaining accountable for practical implementation quality, including code maintainability, system performance, reliability, security, scalability, and cost efficiency Establish engineering best practices through hands-on contribution, code reviews, technical design reviews, automated testing, observability, monitoring, and operational excellence Champion machine learning operations practices including model lifecycle management, prompt versioning, automated evaluation, deployment pipelines, monitoring, and continuous…