Principal AI Engineer

Description and Requirements

As an AI & Data Science Engineer focused on enterprise AI platform development, you will play a pivotal role in architecting, designing, and implementing the core AI and machine learning (ML) infrastructure that underpins our next-generation business applications. You will collaborate with cross-functional teams–including enterprise platform and architecture team, data engineers, Dev Ops, and business stakeholders–to deliver solutions that are not only technically sound but also aligned with the strategic goals of the organization.

As an AI & Data Science Engineer focused on enterprise AI platform development, you will play a pivotal role in architecting, designing, and implementing the core AI and machine learning (ML) infrastructure that underpins our next-generation business applications. You will collaborate with cross-functional teams–including enterprise platform and architecture team, data engineers, Dev Ops, and business stakeholders–to deliver solutions that are not only technically sound but also aligned with the strategic goals of the organization.

• AI Platform Capability Development:
  Design, develop, and manage scalable enterprise AI capabilities that support the AI platform.
• Model Engineering:
  Design, train, and optimize machine learning and deep learning models for a variety of business use cases (e.g., SLM, computer vision, predictive analytics, recommendation systems).
• Data Pipeline Engineering:
  Collaborate with data engineers to design and implement robust data ingestion, transformation, validation, and feature engineering pipelines tailored for AI/ML workloads.
• Platform Integration:
  Enable seamless integration of AI capabilities into business applications and workflows through APIs, SDKs, and microservices.
• Automation & MLOps:
  Develop and maintain CI/CD pipelines for ML models, ensuring automated testing, versioning, deployment, and monitoring in production environments.
• Performance & Scalability:
  Optimize platform components for efficiency, scalability, and reliability using best practices in distributed computing, resource management, and cloud-native architectures.
• Security & Compliance:
  Implement and advocate for security, privacy, ethical, and compliance best practices throughout the AI/ML platform and model development lifecycle.
• Innovation & Research:
  Stay abreast of the latest trends and advancements in AI, ML, and platform engineering. Evaluate and prototype emerging technologies for inclusion in the enterprise platform.
• Documentation & Knowledge Sharing:
  Produce clear technical documentation and participate in knowledge sharing to mentor team members and enable collaboration across the organization.

• Bachelor’s or Master’s degree in computer science, Data Science, Engineering, Mathematics, or a related field. A PhD is a plus.
• 7 years of experience in AI/ML engineering, GenAI, data science, or related roles.
• Proficiency in machine learning frameworks and libraries (e.g., Tensor Flow, PyTorch, Scikit-learn, XGBoost, Hugging Face).
• Strong software engineering skills with experience in Python (required).
• Proven experience designing, developing, and deploying Generative AI (GenAI) solutions using large language models (LLMs) such as GPT, Llama, Claude, etc.
• Experience with modern GenAI frameworks and libraries (e.g., Lang Chain, Llama Index, Hugging Face Transformers).
• Familiarity with best practices for responsible AI, including data privacy, bias mitigation, and model monitoring.

• Experience with cloud platforms (AWS, Azure, Google Cloud Platform), containerization (Docker, Kubernetes), and orchestration tools.
• Hands‑on expertise with Retrieval‑Augmented Generation (RAG) architectures, including integrating external data sources and vector databases to enhance LLM outputs.
• Strong understanding of prompt engineering, fine‑tuning, and evaluation of generative models for real‑world applications.
• Ability to build, optimize, and scale GenAI pipelines for tasks such as document Q&A, summarization, chatbots, and knowledge retrieval.

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