AI & ML

The Principal Knowledge Graph Engineer designs, builds, and operationalizes enterprise-grade knowledge graph capabilities that connect data, concepts, and relationships to power AI-driven experiences such as search, recommendations, analytics, and agentic workflows. This role blends deep graph engineering, semantic modeling, and production software engineering to deliver a governed, performant, and evolvable “knowledge layer” across products and internal platforms.

The **Principal Generative AI Engineer** is a senior individual-contributor (IC) engineering leader responsible for designing, building, and operationalizing generative AI capabilities (LLM-powered features, agentic workflows, and internal AI platforms) that are secure, reliable, and cost-effective at enterprise scale. The role sits at the intersection of software engineering, applied ML, and platform engineering—translating business problems into production-ready architectures and guiding teams to deliver measurable outcomes.

The **Principal Federated Learning Engineer** is a senior individual contributor who designs, builds, and governs **privacy-preserving distributed machine learning** systems that enable model training across multiple data owners (devices, customers, business units, or partners) without centralizing sensitive data. The role exists to unlock high-value ML use cases where data cannot legally, contractually, or ethically be pooled—while still achieving strong model performance, reliability, and measurable business outcomes.

The **Principal Edge AI Engineer** is a senior individual contributor (IC) responsible for architecting, delivering, and operationalizing **machine learning inference and intelligent decisioning on edge devices** (e.g., gateways, industrial PCs, retail devices, mobile/embedded endpoints) where constraints such as latency, connectivity, privacy, power, and cost materially shape the solution. This role designs the end-to-end edge AI “production system”: model packaging and optimization, device runtime architecture, secure deployment and updates, observability, and continuous improvement loops.

The **Principal Computer Vision Engineer** is a senior individual contributor who defines and delivers computer vision (CV) capabilities that become reliable, scalable product features and/or internal platforms. This role owns end-to-end technical outcomes—from problem framing and data strategy through model development, optimization, deployment, monitoring, and iterative improvement—while setting engineering standards for CV across teams.

The Principal Autonomous Systems Engineer is a senior individual-contributor (IC) engineering role responsible for designing, validating, and scaling autonomy capabilities (perception, prediction, planning, control, and autonomy orchestration) that operate reliably in complex, real-world environments. This role blends advanced software engineering, applied ML, systems architecture, and safety-minded engineering to deliver end-to-end autonomous behaviors that meet product requirements and operational constraints.

The Principal Applied AI Engineer is a senior individual contributor who designs, builds, and scales production-grade AI systems that deliver measurable business outcomes. This role bridges advanced machine learning and software engineering: translating ambiguous product needs into reliable, secure, observable services and pipelines that can be operated at enterprise scale.

The **Principal AI Safety Engineer** is a senior individual contributor responsible for designing, implementing, and operationalizing technical safeguards that reduce safety, security, and misuse risks in AI/ML systems—especially large language model (LLM) and generative AI products. The role blends deep engineering expertise with applied risk thinking to ensure AI systems behave reliably under real-world conditions, including adversarial use.

The **Principal AI Platform Engineer** is a senior individual-contributor (IC) engineering leader responsible for designing, building, and evolving the internal platform capabilities that enable teams to develop, deploy, operate, and govern machine learning (ML) and generative AI solutions safely and efficiently at enterprise scale. This role unifies **platform engineering**, **MLOps**, **LLMOps**, **reliability engineering**, and **AI governance-by-design** into a coherent “paved road” that accelerates delivery while reducing operational and compliance risk.

The **Principal AI Evaluation Engineer** designs, implements, and governs the evaluation systems that determine whether AI models (including LLMs and traditional ML) are *safe, effective, reliable, and fit for production use*. This role establishes enterprise-grade evaluation methodology—offline benchmarks, online experimentation, human-in-the-loop scoring, and continuous monitoring—to reduce model risk and accelerate high-confidence releases.

The **Principal AI Engineer** is a senior, hands-on technical leader responsible for designing, building, and operating production-grade AI/ML (including GenAI where applicable) capabilities that materially improve product outcomes, internal productivity, and platform differentiation. This role bridges applied machine learning, software engineering, and reliable operations—ensuring models and AI services are safe, scalable, measurable, and maintainable.

The **Principal AI Agent Engineer** is a senior individual contributor who designs, builds, and operationalizes **agentic AI systems**—LLM-driven applications that can plan, use tools, execute multi-step workflows, and collaborate with humans and services safely and reliably. This role exists to turn rapidly evolving agent frameworks and foundation models into **production-grade capabilities** that create measurable business impact while meeting enterprise expectations for security, quality, and cost control.

The NLP Engineer designs, builds, evaluates, and operates natural language processing (NLP) capabilities that power product features and internal platforms, such as semantic search, summarization, classification, extraction, conversational experiences, and content safety. The role blends applied machine learning engineering with production-grade software engineering to deliver reliable, secure, and measurable language intelligence.

The **Multi-Agent Systems Engineer** designs, builds, and operates software systems where multiple AI agents (often LLM-powered) coordinate to accomplish complex workflows—planning, tool use, delegation, verification, and iterative improvement—within production-grade applications. The role blends applied machine learning, distributed systems thinking, and product engineering to turn agent research patterns into reliable, secure, cost-effective capabilities.

The **Model Validation Engineer** is an individual contributor engineering role responsible for independently assessing, testing, and challenging machine learning (ML) models before and after deployment to ensure they are accurate, robust, explainable, and safe for production use. This role designs and executes validation methodologies (offline evaluation, bias/fairness checks, stress testing, drift monitoring, and reproducibility verification) and translates findings into actionable engineering and product decisions.

A **Model Risk Engineer** designs, implements, and operates the technical controls that reduce risk in machine learning (ML) and generative AI systems across their lifecycle—from data ingestion and training through deployment, monitoring, and retirement. The role bridges **software engineering, MLOps, and responsible/secure AI** by turning risk requirements (fairness, privacy, robustness, security, explainability, and compliance) into **repeatable engineering systems** and measurable guardrails.

A **Model Operations Engineer** designs, builds, and runs the production-grade systems and operating practices that allow machine learning (ML) models to be deployed safely, monitored continuously, and improved reliably over time. The role sits at the intersection of software engineering, platform operations, and applied ML—translating data science outputs into **durable, observable, compliant** services that deliver business value in real products.

The Model Evaluation Engineer designs, implements, and operationalizes how machine learning (ML) and AI models are measured, compared, validated, and continuously monitored across their lifecycle—from offline experimentation to production performance and safety. The role exists to ensure model quality is not anecdotal or ad hoc, but governed by repeatable evaluation methods, reliable datasets, robust metrics, and automated test harnesses that prevent regressions and support trustworthy releases.

The **MLOps Engineer** designs, builds, and operates the end-to-end systems that reliably deliver machine learning models into production. This role connects data science experimentation with production-grade engineering by standardizing pipelines, automating deployments, implementing model monitoring, and ensuring that ML workloads meet reliability, security, and compliance expectations.

The Machine Learning Engineer (MLE) designs, builds, deploys, and operates machine learning systems that deliver measurable product and business outcomes in a production software environment. This role bridges data science and software engineering by turning models and experimentation into reliable, observable, secure, and scalable services and pipelines.

The **LLMOps Engineer** designs, builds, and operates the platforms and pipelines that make Large Language Model (LLM) features reliable, secure, cost-effective, and measurable in production. This role sits at the intersection of **ML platform engineering, DevOps/SRE practices, and applied LLM product delivery**, ensuring that experimentation turns into governed, observable, and repeatable deployments.

The **LLM Quality Engineer** is responsible for ensuring that large language model (LLM) features and systems behave reliably, safely, and measurably well in production. This role builds and operates the evaluation, testing, and monitoring capabilities required to prevent regressions, quantify quality, and improve user outcomes across LLM-powered products (e.g., chat assistants, summarization, search/RAG, workflow automation).

The **LLM Engineer** designs, builds, evaluates, and operates software capabilities powered by large language models (LLMs), translating product needs into reliable, secure, and cost-effective AI-driven experiences. The role sits at the intersection of machine learning engineering, backend engineering, and applied research—focused less on inventing new foundational models and more on **productionizing** LLM solutions (e.g., RAG, tool/function calling, fine-tuning, evaluation, and governance).

The Lead Synthetic Data Engineer designs, builds, and operationalizes synthetic data capabilities that enable AI/ML development, testing, and analytics when real data is scarce, sensitive, biased, or operationally expensive to use. The role owns the end-to-end synthetic data lifecycle—data understanding, generation method selection, privacy/utility evaluation, production pipelines, and governance—so synthetic datasets are trustworthy, repeatable, and fit for purpose.

The **Lead Robotics Software Engineer** is the technical lead responsible for designing, building, integrating, and operating the software that enables robotic systems to perceive, plan, and act safely and reliably in real-world environments. This role typically owns critical parts of a robotics autonomy stack (e.g., perception, localization, motion planning, controls, fleet management, simulation, and runtime infrastructure) while setting engineering standards and mentoring a small team of robotics engineers.

The Lead Responsible AI Engineer ensures that AI/ML systems—especially generative AI (GenAI) and decision-support models—are designed, built, deployed, and operated with measurable safeguards for safety, fairness, privacy, security, transparency, and regulatory compliance. This role combines deep ML engineering and MLOps capability with risk-based governance, enabling product teams to ship AI features faster while reducing harm, audit exposure, and operational surprises.

The **Lead Recommendation Systems Engineer** designs, builds, and operates large-scale recommendation and ranking systems that meaningfully influence user engagement, retention, and revenue. This role blends applied machine learning, distributed systems engineering, experimentation, and product thinking to deliver personalized experiences in production with measurable business impact.

The Lead RAG Engineer designs, builds, and operates Retrieval-Augmented Generation (RAG) systems that reliably connect large language models (LLMs) to enterprise knowledge and product data. This role exists to turn unstructured and semi-structured organizational information into governed, secure, low-latency retrieval services that materially improve accuracy, freshness, and trustworthiness of AI-assisted experiences.

The Lead NLP Engineer is a senior, hands-on engineering leader responsible for designing, building, and operating production-grade Natural Language Processing (NLP) systems that power customer-facing and internal AI capabilities. This role bridges applied research and software engineering, translating language model capabilities into reliable, secure, cost-effective services integrated into products and enterprise workflows.

The **Lead MLOps Engineer** designs, builds, and runs the production-grade systems that reliably deliver machine learning models into customer-facing and internal products. This role turns research-quality models into **secure, observable, scalable, cost-efficient** services and pipelines, while establishing repeatable standards for model delivery and operations across the AI & ML department.