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A **Staff RAG Engineer** designs, builds, and operates retrieval-augmented generation (RAG) systems that reliably ground large language model (LLM) outputs in enterprise data. The role sits at the intersection of applied ML, software engineering, information retrieval, and platform reliability—owning the end-to-end lifecycle from data ingestion and indexing through retrieval, prompting/orchestration, evaluation, and production operations.

The **Staff NLP Engineer** is a senior individual contributor (IC) responsible for designing, building, and operationalizing natural language processing (NLP) and large language model (LLM) capabilities that power customer-facing product experiences and internal intelligence workflows. This role owns the technical approach for complex language problems—such as search relevance, summarization, conversational interfaces, classification, and retrieval-augmented generation (RAG)—and ensures solutions meet enterprise standards for reliability, privacy, and cost.

The **Senior RAG Engineer** designs, builds, and operates **retrieval-augmented generation (RAG)** systems that connect large language models (LLMs) to enterprise knowledge and product data—safely, reliably, and cost-effectively. The role exists to move LLM use cases from prototypes to **production-grade AI capabilities** with measurable quality (groundedness, relevance, accuracy), robust governance, and operational excellence.

The **Senior AI Agent Engineer** designs, builds, and operates **LLM-powered agents** that can plan, use tools, retrieve enterprise knowledge, and complete multi-step tasks reliably in production. The role sits at the intersection of software engineering, applied ML, product integration, and operational excellence—turning foundation models into **safe, observable, cost-controlled, and measurable** user-facing capabilities.

A **RAG Engineer** designs, builds, and operates **Retrieval-Augmented Generation (RAG)** systems that connect large language models (LLMs) to enterprise knowledge—enabling accurate, grounded, and secure answers inside products and internal tools. This role exists because LLMs alone are not sufficient for most enterprise use cases: the business needs **fresh, permissioned, auditable, and domain-specific** responses backed by trusted sources and measurable performance.

The **Principal Robotics Software Engineer** is a senior individual-contributor (IC) technical leader responsible for designing, building, and evolving the software foundations that enable reliable robotic autonomy at scale—typically across perception, localization, planning, control, and the runtime platform that orchestrates these capabilities. The role blends deep robotics engineering expertise with software architecture, production-grade quality practices, and cross-functional technical leadership across AI & ML, product, and operations.

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 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.

A **Knowledge Graph Engineer** designs, builds, and operates knowledge graph (KG) systems that connect disparate enterprise data into a unified, queryable, and semantically meaningful representation. The role blends data engineering, graph modeling, ontology design, and applied AI techniques to enable better search, recommendations, analytics, reasoning, and AI applications (including LLM-augmented experiences).

The **Junior Synthetic Data Engineer** builds, tests, and operates early-stage capabilities that generate **high-utility synthetic datasets** for machine learning development, testing, analytics, and privacy-preserving data sharing. The role focuses on implementing repeatable pipelines, evaluation methods, and documentation so synthetic data can be safely used by product and engineering teams without exposing sensitive source data.

The **Junior Recommendation Systems Engineer** builds, evaluates, and supports machine learning–driven recommendation and ranking components that personalize user experiences across digital products. The role focuses on implementing well-scoped modeling and data tasks, improving feature pipelines, running offline and online evaluations, and contributing to production-quality ML services under guidance from senior engineers and applied scientists.

The **Junior RAG Engineer** builds, tests, and improves **Retrieval-Augmented Generation (RAG)** components that help product experiences answer questions and generate content grounded in trusted company data. This role focuses on implementing retrieval pipelines, chunking and embedding strategies, prompt templates, and evaluation harnesses under the guidance of senior engineers and applied scientists.

The **Junior Generative AI Engineer** builds, tests, and iterates on early production and pre-production generative AI capabilities—most commonly **LLM-powered features** such as retrieval-augmented generation (RAG), summarization, search augmentation, document understanding, and workflow copilots—under the guidance of senior engineers and applied scientists. This role focuses on reliable implementation: turning prototypes into maintainable services, integrating with product surfaces, and applying evaluation and safety guardrails.

The Junior Edge AI Engineer builds, optimizes, and deploys machine learning models that run on edge devices (e.g., IoT gateways, embedded Linux devices, industrial PCs, mobile, cameras) where latency, connectivity, power, and privacy constraints require on-device intelligence. This role exists in a software or IT organization to operationalize AI in real-world environments—delivering reliable inference close to where data is generated instead of relying solely on cloud processing. Business value comes from lower latency, reduced cloud cost, improved resilience during network outages, and enhanced privacy/security by minimizing data egress.

A **Junior AI Safety Engineer** supports the safe, reliable, and policy-compliant development and deployment of machine learning (ML) and generative AI (GenAI) systems by implementing safety evaluations, mitigations, and monitoring controls within engineering workflows. The role focuses on practical engineering work: building and running test harnesses, creating safety checks in CI/CD, helping triage safety incidents, and partnering with senior safety engineers, applied scientists, and product teams to reduce harmful or non-compliant model behaviors.

The **Associate RAG Engineer** builds and improves retrieval‑augmented generation (RAG) capabilities that connect large language models (LLMs) to trusted enterprise knowledge (documents, tickets, product data, policies) to produce accurate, grounded answers. This role focuses on implementing retrieval pipelines, preparing and indexing content, evaluating answer quality, and supporting productionization under guidance of senior engineers.

The **Associate Machine Learning Engineer** builds, tests, and operationalizes machine learning components that power software products and internal platforms. This role sits at the intersection of software engineering and applied machine learning, contributing production-ready code, reproducible experiments, and reliable model deployment workflows under the guidance of senior ML engineers and data science leaders.

The **Associate Applied AI Engineer** designs, builds, and supports AI-enabled features and services that solve clearly defined product or operational problems, using established machine learning (ML) and software engineering practices. This role sits at the intersection of ML implementation and production software delivery: translating use cases into deployable model-backed components, evaluation pipelines, and measurable product outcomes.

The **AI Security Engineer** designs, implements, and operates security controls that protect AI/ML systems across the full lifecycle—data, training, evaluation, deployment, inference, and monitoring. The role focuses on preventing and detecting AI-specific threats (e.g., data poisoning, model theft, prompt injection, insecure tool use in agents, supply-chain compromise) while integrating with standard application and cloud security practices.

The **AI Red Team Engineer** proactively identifies, validates, and helps mitigate security, safety, and misuse risks in AI systems—especially **LLM-powered products**, AI agents, and ML-enabled features—before those risks impact customers or the business. The role blends adversarial engineering, applied security testing, and practical ML/LLM understanding to uncover failure modes such as jailbreaks, prompt injection, data leakage, harmful content generation, and tool/agent misuse.