Senior Principal Software Engineer

Job Summary:

The Red Hat Ecosystems Engineering group is seeking a Senior Principal Software Engineer in our Boston, MA office.  In this role, you will work with a diverse team of highly motivated engineers on designing and implementing AI/ML workflows and solutions and integrating Partners solutions. You will also be working closely with the product management, other engineering groups within Red Hat, and with Red Hat partners and lighthouse customers.

At Red Hat, our commitment to open source innovation extends beyond our products - it’s embedded in how we work and grow. Red Hatters embrace change – especially in our fast-moving technological landscape – and have a strong growth mindset. That's why we encourage our teams to proactively, thoughtfully, and ethically use AI to simplify their workflows, cut complexity, and boost efficiency. This empowers our associates to focus on higher-impact work, creating smart, more innovative solutions that solve our customers' most pressing challenges.

Are you ready to start developing new solutions that combine open source, hybrid cloud, and AI?

What you will do:

• Architect, design, document, and develop applications in Python and AI/ML frameworks

• Architect and lead the implementation of Agentic AI applications

• Architect and lead implementation of scalable solutions with distributed computing capabilities  to deploy, train, and serve ML models  on OpenShift AI (RHOAI)

• Develop and optimize retrieval-augmented generation (RAG) pipelines 

• Drive end-to-end execution of small-scale, cross-team and partner initiative

• Build multi product demos and AI/ML workflows using Predictive and Generative AI leveraging RH product and AI stack

• Work with upstream AI/ML communities to evaluate new AI/ML-related technologies from partners and create examples of integrations between their technology and RHOAI

• Collaborate with AI/ML partners  to adjust their AI strategies, address their specific use cases, and drive value through the adoption of the RHOAI 

• Demonstrate proficiency in utilizing LLMs (e.g., Google Gemini), as relevant, for tasks such as brainstorming solutions, deep research, summarizing technical documentation, drafting communications, summarizing complex technical information, and enhancing problem-solving efficiency across the development lifecycle

• Explore and experiment with emerging AI technologies relevant to software development, proactively identifying opportunities to incorporate new AI capabilities into existing workflows and tooling.

• Collaborate with cross-functional teams to identify opportunities for AI integration within the software development lifecycle, driving continuous improvement and innovation in engineering practices; share use cases for successful experiments with stakeholders for broader use

What you will bring:

• Minimum of 10 years of relevant technical experience in software development, at least a year using Python and Go

• Experience of containers and OpenShift or Kubernetes 

• Experience and knowing AI frameworks  and libraries (e.g. OpenDataHub, TensorFlow, PyTorch, Kueue, KubeRay, KubeFlow, CodeFlare, Feast etc)

• Familiarity with model parallelization, quantization, and memory optimization using vLLM, DeepSpeed, OpenVino and other inference libraries

• Strong experience with microservices architectures and concepts including APIs, versioning, monitoring, etc.

• Experience with AI/ML technologies, including foundational frameworks, large language models (LLMs), Retrieval Augmented Generation (RAG) paradigms, vector databases, and LLM orchestration tool

• Experience  with AI and MLOps tools and Concepts, including Automation, GitOps, pipelines, models, etc. for managing the AI/ML lifecycle in production environments

• Interest in learning new technologies; problem-solving skills

The following are considered a plus: 

• Cloud Computing experience using at least one of the following Cloud infrastructures AWS, GCP, Azure & IBM Cloud

• Previous code contributions to or participation in open source projects or code samples on GitHub. 

• Contributions to internal AI platforms, model evaluation frameworks, or observability for AI systems

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