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.

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.

• 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 (Tensor Flow, 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.

• 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 (Tensor Flow, 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, Git Ops, and Dev Sec Ops  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.

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