Principal Senior AI Engineer

About the Role

Key Responsibilities

• Lead the technical design and architecture of AI solutions for the AI Lab.
• Define AI solution patterns, including LLM integration, RAG, vector databases, AI agents, workflows, model routing, and model orchestration.
• Evaluate, benchmark, and select suitable AI models, including cloud-based, private cloud, and on-premise models.
• Guide the development of AI prototypes, proof of concepts, and production-ready AI systems.
• Review and validate AI pipelines, prompts, model outputs, integrations, deployment approaches, and evaluation results.
• Provide technical leadership and mentoring to AI engineers, software engineers, data engineers, and implementation teams.
• Support AI use-case discovery, feasibility assessment, technical scoping, architecture planning, and implementation roadmaps.
• Ensure AI solutions follow security, privacy, governance, auditability, and enterprise architecture standards.
• Work with infrastructure teams to define hosting, GPU, deployment, scaling, monitoring, and operational requirements.
• Support the implementation of AI agents, AI assistants, automation workflows, and intelligent decision support systems.
• Establish AI engineering best practices, reusable components, documentation standards, and development guidelines.
• Review vendor solutions, open-source frameworks, third-party AI platforms, and integration approaches.
• Support management with technical recommendations, delivery risks, limitations, mitigation actions, and execution plans.

Required Experience

• 10+ years of experience in software engineering, AI engineering, machine learning, data science, or related technical roles.
• Strong experience designing and delivering enterprise AI solutions and production-grade systems.
• Hands-on experience with LLMs, RAG, vector databases, embeddings, prompt engineering, and AI agent architectures.
• Experience with model deployment, model evaluation, benchmarking, inference optimization, and performance tuning.
• Strong understanding of cloud, private cloud, and on-prem AI deployment models.
• Experience with APIs, microservices, system integration, data platforms, and enterprise software architecture.
• Experience leading technical teams or operating as a principal, lead, staff, or architecture-level engineer.
• Strong understanding of data privacy, cybersecurity, access control, AI governance, and responsible AI practices.

Technical Skills

• Python and modern backend frameworks such as FastAPI or equivalent.
• LLM ecosystems such as OpenAI, Azure OpenAI, Qwen, Llama, Mistral, Claude, or similar model families.
• RAG frameworks such as LangChain, LlamaIndex, Semantic Kernel, Haystack, or equivalent.
• Vector and search technologies such as Elasticsearch, OpenSearch, Qdrant, Milvus, Pinecone, Weaviate, or equivalent.
• Docker, Kubernetes, CI/CD, DevOps practices, and enterprise deployment pipelines.
• GPU-based inference, model serving, local model hosting, or high-performance AI infrastructure experience is preferred.
• MLOps, observability, evaluation frameworks, tracing, logging, and AI monitoring.
• Familiarity with .NET, JavaScript/TypeScript, enterprise backend systems, or API gateway patterns is a plus.

Soft Skills & Competencies

• Strong leadership and ownership mindset.
• Ability to translate business requirements into practical AI architectures and implementation plans.
• Strong communication skills with technical teams, executives, business users, vendors, and security stakeholders.
• Ability to mentor engineers, review technical work, and raise engineering standards.
• Strong problem-solving, analytical thinking, and decision-making capabilities.
• Ability to work in secure, sensitive, regulated, and enterprise environments.