Principal Software Engineer - AI Engineer
Role details
Job location
Tech stack
Job description
- Design and implement agentic AI reference architectures, including orchestration, retrieval, memory, guardrails, and evaluation harnesses.
- Write production-quality Python code (PyTorch or TensorFlow as needed) and review critical-path code
- Create reusable components for prompt management, evaluators, safety filters, connectors, embeddings pipelines, and memory stores
- Build and operate LLM-powered APIs and microservices integrated into advisor, client, and internal workflows
- Own the end-to-end ML lifecycle: experimentation, CI/CD, automated testing, monitoring, drift detection, versioning, and rollback
- Optimize inference for latency, throughput, caching, batching, model selection, and cost per inference
- Partner with data teams on structured and unstructured data pipelines, document ingestion, metadata, and access controls
- Set engineering standards for agentic AI systems and lead design reviews
- Influence roadmap and priorities through technical insight and delivery
- Architects and governs agentic AI-enabled engineering workflows (using enterprise-authorized tools within the work environment) to improve delivery speed, code quality, and operational outcomes at scale (e.g., AI-driven PR review assistance, test generation/maintenance, release readiness checks, incident triage and root-cause acceleration), while defining guardrails for validation, security, resiliency, and reuse across teams.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation at scale.
Requirements
- Formal training or certification on software engineering concepts and 7+ years applied experience
- Strong Python engineering skills; experience with PyTorch or TensorFlow
- Expertise working with Vector storage systems and designing memory for Agents
- Expertise developing long running agents that run autonomously using tools, skills and human in the loop
- Proven experience deploying LLM-backed services to production (APIs, microservices)
- Deep MLOps experience, including CI/CD, monitoring, incident response, and model governance
- Cloud-native AI deployment experience (AWS or Azure), with cost and performance optimization
- Demonstrated commitment to responsible AI practices and operational excellence
- Strong communication and collaboration skills, working across product, risk, legal, and compliance teams
- Demonstrated experience designing and leading adoption of agentic AI-enabled development practices (using enterprise-authorized tools within the work environment) across teams, including setting standards for human-in-the-loop validation, auditability/traceability of changes, and secure handling of sensitive data.
- Strong understanding of responsible AI use and control expectations in engineering workflows, including security/resiliency implications, data sensitivity, and risk-based governance; ability to influence senior technical leaders on safe scaling patterns and reuse.
Preferred Qualifications, Capabilities, and Skills:
- Experience with fine-tuning, adapters, or custom evaluation frameworks
- Background operating AI systems in regulated environments (finance, healthcare, etc.)
- Experience with prompt engineering and LLM orchestration
- Knowledge of safety filters, audit logging, and explainability in production systems
- Experience mentoring senior engineers and leading architecture discussions
- Demonstrated ability to influence technical roadmaps and priorities
Benefits & conditions
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.