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.

The Lead Machine Learning Engineer is a senior technical leader responsible for designing, building, deploying, and operating production-grade machine learning systems that deliver measurable business outcomes. The role blends advanced ML engineering with strong software engineering, MLOps, and cross-functional leadership to ensure models are reliable, scalable, secure, and maintainable in real-world environments.

The **Lead LLM Engineer** is a senior engineering leader (primarily an advanced individual contributor with team technical leadership) responsible for designing, building, and operating **LLM-powered capabilities** that are reliable, secure, cost-efficient, and measurably useful in production. This role owns the end-to-end technical approach for LLM applications—spanning retrieval-augmented generation (RAG), agentic workflows, evaluation, safety controls, and LLMOps—turning model capabilities into dependable product and internal platform services.

The **Lead Knowledge Graph Engineer** designs, builds, and operationalizes knowledge graph (KG) capabilities that connect an organization’s data into an interpretable, queryable, and machine-reasonable layer to power AI, analytics, and product experiences. This role sits at the intersection of **data engineering, semantic modeling, graph systems, and applied ML**, translating messy enterprise data into high-quality entities, relationships, and ontologies that can be reliably used in production.

The **Lead Generative AI Engineer** is a senior technical leader responsible for designing, building, and operating production-grade generative AI (GenAI) capabilities—such as LLM-powered features, retrieval-augmented generation (RAG) systems, and agentic workflows—while ensuring reliability, security, cost control, and measurable business outcomes. This role bridges advanced ML engineering with modern software engineering practices to take GenAI from prototypes to scalable, governed, observable services.

The **Lead Federated Learning Engineer** designs, builds, and operationalizes federated learning (FL) capabilities that enable machine learning models to be trained across distributed data sources (devices, edge nodes, partner environments, or business units) **without centralizing raw data**. This role blends advanced applied ML with distributed systems engineering, privacy-preserving computation, and production MLOps to deliver scalable, secure, and measurable FL deployments.

The **Lead Edge AI Engineer** designs, builds, and operates machine learning (ML) inference capabilities that run **on-device or near-device** (edge gateways, embedded systems, edge clusters) with strict constraints on latency, compute, power, privacy, and reliability. This role turns ML models into **production-grade edge AI services** by optimizing models, selecting runtime stacks, building secure deployment pipelines, and ensuring observability and lifecycle management across heterogeneous hardware fleets.