Principal Al Engineer

About Cornerstone: Cornerstone powers the future-ready workforce with modern, AI-driven employment solutions. Our platform enables companies to develop, manage, and engage their talent—unlocking growth and innovation across organizations of all sizes. Who We’re Looking For: Cornerstone is seeking a visionary and highly accomplished Distinguished Principal AI Engineer to spearhead the creation of groundbreaking AI and machine learning solutions across our industry-leading workforce agility - Galaxy ecosystem. This is a rare opportunity for a true innovator—someone who thrives on architecting and hands-on building intelligent, autonomous systems at massive scale. You’ll be driving the future of workforce technology by delivering AI-powered applications that are robust, secure, ethical, and brilliantly performant. In this role you will… Full-Stack AI Engineering: Lead the hands-on development, deployment, and continuous improvement of sophisticated AI-driven features, leveraging Agile practices and top-tier coding standards. Advanced Architecture System Design: Architect, implement, and scale modern, distributed AI systems—including training pipelines, streaming data processing, serverless microservices, and MLOps infrastructure—to deliver enterprise-grade reliability and security. ML Model Innovation: Expertly design, build, and tune production AI/ML models (NLP, Deep Learning, Recommender Systems, LLMs, Generative AI) using cutting-edge frameworks (TensorFlow, PyTorch, Hugging Face, Keras, Scikit-Learn, Ray). Cloud Data Engineering Mastery: Develop and optimize cloud-native (AWS, GCP, Azure) AI workloads—utilizing Kubernetes, Docker, Spark, and high-performance data lakes for advanced data wrangling, batch and real-time inference, and model monitoring. Agentic Generative AI Technologies: Design and deploy intelligent, autonomous AI agents (LLMs, multi-agent systems) capable of planning, reasoning, and decision-making—solving complex HR and talent management challenges with next-gen AI. Orchestration Tooling: Build frameworks for multi-agent orchestration, message passing, prompt engineering, vector databases (FAISS, Pinecone), and scalable knowledge graphs to enable robust agent collaboration and negotiation. Task Automation Workflow AI: Develop specialized AI agents for process automation—streamlining content generation, personalized recommendations, and end-to-end workflow optimization using RPA and conversational AI. Safety, Reliability, Explainability: Set gold standards for AI safety, fairness, and explainability—implementing evaluation protocols, guardrails, and bias detection to ensure ethical agent behavior in real-world deployments. Seamless Systems Integration: Fuse agentic and generative AI systems with modern APIs, REST/gRPC, user interfaces (React, Angular), microservices, and enterprise data sources for resilient, scalable solutions. Performance Tuning MLOps: Apply best-in-class techniques for model performance, hyperparameter optimization, scalable retraining, monitoring, and CI/CD for AI pipelines. Research, Innovation Thought Leadership: Stay at the cutting edge with constant exploration of new AI technologies—transforming foundational research into impactful product features. Standards Advocacy: Champion software engineering excellence—driving best practices in secure coding, peer review, and responsible AI design throughout the full SDLC. You’ve got what it takes if you have… Bachelor’s, Master’s, or PhD in Computer Science, Engineering, Machine Learning, or related field. 5+ years in software engineering with a minimum of 2+ years hands-on building, deploying, and optimizing AI/ML applications at enterprise scale. Deep expertise in AI/ML model development (NLP, Deep Learning, LLMs, Recommender Systems, Generative AI) and their deployment in cloud production environments. Advanced hands-on proficiency with cloud platforms (AWS, Azure, GCP), ML frameworks (TensorFlow, PyTorch, Hugging Face, Scikit-Learn), modern programming languages (Python, Java, Scala, C++), and distributed systems (Kubernetes, Docker, Spark). Strong foundation in system architecture, algorithm design, scalable data engineering (ETL, batch stream processing), and model serving. Experience with modern MLOps, CI/CD, GitOps, and DevSecOps methodologies. Commitment to ethical, responsible AI—deep understanding of privacy, explainability, bias, and regulatory considerations. Prior experience in HR tech, SaaS, or enterprise software highly advantageous. #LI-Onsite