Note: The job is a remote job and is open to candidates in USA. Maxonic Inc. is seeking a Senior Principal AI Engineer to serve as the principal technical authority for its Commercial AI Center of Excellence. The role owns the technical vision for enterprise-scale AI-as-a-Service, including AI architecture, model training, retrieval, orchestration, evaluation, safety, and production operations, while leading complex initiatives and mentoring senior engineers.
Responsibilities
- Serve as the most senior individual-contributor engineer and principal technical authority within the Commercial AI Center of Excellence (CAI CoE), owning the technical vision for AI-as-a-Service (AIaaS) enablement at enterprise scale
- Operate as a full-spectrum AI engineer fluent across the entire lifecycle — data, model training and fine-tuning, retrieval, orchestration, evaluation, and production operations — able to go as deep as any specialist and to compose the pieces into coherent, production-grade systems
- This role requires both traditional AI/ML, GenAI product engineering, and traditional software architecture skillsets
- Establish reference architectures, paved-road patterns, and enterprise technical standards for agentic orchestration, tool and MCP design, retrieval, model training, and responsible AI across production systems
- Lead the most complex, ambiguous, cross-team initiatives spanning multiple value streams, and review and influence AI designs across product teams to ensure alignment with enterprise standards
- Set long-term technical direction while remaining deeply hands-on with the most critical AI infrastructure and services
- Set the multi-year technical vision, reference architectures, and enterprise standards for AIaaS across PDD, and act as the final technical authority and escalation point for the organization''s hardest AI problems
- Define and own the enterprise model-training strategy across traditional AI/ML and LLMs, and personally train, fine-tune (e.g., QLoRA, LoRA, PEFT, and full fine-tuning), and evaluate models when the problem demands it
- Establish standards for how and where training data from Commercial AI products is sourced, cleaned, versioned, stored, and governed (lineage, licensing/consent, and PII), and architect the large-scale data and feature pipelines behind them
- Architect the orchestration and abstraction layers of the central AI system that connect LLMs to tools, data, and sub-agents, and set standards for MCP servers, tool-surface design (optimal number of tools exposed per LLM and APIs per server), and when to use specialized sub-agents versus direct tool exposure
- Design retrieval/RAG systems end to end — chunking strategies, embeddings, vector stores, hybrid search, re-ranking, context assembly, and memory
- Own the enterprise evaluation, observability, and safety strategy for AI systems, including offline and online evaluation, tracing, red-teaming, guardrails, and responsible-AI and compliance requirements
- Drive build-versus-buy, model and vendor selection, and long-term architectural bets, anticipating where the field is heading and preparing the organization to adopt it
- Optimize the performance, cost (token and inference economics), scalability, and reliability of AI workloads in partnership with Security, Cloud Platform, and SRE teams
- Multiply the organization: mentor and grow Principal and Staff engineers and raise the AI engineering bar
Skills
- 15+ years in AI/ML software engineering with demonstrated Senior Principal-level (or equivalent) impact delivering production AI at enterprise scale
- Full-lifecycle, hands-on mastery across both specialist domains: (a) training and fine-tuning traditional AI/ML models and LLMs — including parameter-efficient methods (QLoRA/LoRA/PEFT), quantization, distributed training, and rigorous evaluation; and (b) LLM orchestration, agentic systems, tool/MCP design, and retrieval/RAG in production
- Deep expertise in distributed systems, cloud-native architecture, and large-scale data and feature pipelines
- Strong command of data management and governance: dataset storage architecture, versioning, lineage, quality, PII handling, and licensing/consent for training data
- Proven ability to design developer platforms, APIs, reusable SDKs, MCP servers, and multi-agent orchestrations that many teams depend on
- Rigorous, demonstrated approach to AI evaluation, observability and tracing, and responsible-AI guardrails
- Expertise with cloud platforms (Azure strongly preferred) and a track record of optimizing AI workload cost, performance, scalability, and reliability
- Demonstrated ability to set technical strategy and influence decisions across many teams without direct authority, and to mentor Principal- and Staff-level engineers
- Experience operating in regulated SaaS environments and meeting security and compliance requirements
- Bachelor''s degree in Computer Science, Engineering, or a related discipline; advanced degree preferred
- An equivalent combination of education, training, and relevant professional experience is accepted in lieu of a formal degree
- Industry experience in tax or other regulated domains (insurance, fintech, or healthcare)
- Experience with vector databases and retrieval optimization at scale
- FinOps for GenAI: experience modeling and optimizing LLM token and inference costs
- Data science or classical AI background beyond prompt engineering (statistics, feature engineering, and model evaluation)
- Contributions to open-source AI tooling, published research, patents, or recognized technical thought leadership
- Strong executive communication and technical storytelling skills
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