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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 NLP Engineer** builds, evaluates, and improves natural language processing (NLP) capabilities that power user-facing features and internal AI workflows (e.g., classification, extraction, semantic search, summarization, conversational experiences, and retrieval-augmented generation). The role focuses on implementing well-defined solutions under guidance, turning research or prototype concepts into reliable components that can be tested, shipped, and monitored in production.

The **Associate MLOps Engineer** supports the reliable deployment, monitoring, and ongoing operations of machine learning (ML) models and ML-enabled services in production. This role focuses on implementing and maintaining the “last mile” systems that connect data science work to secure, observable, and scalable runtime environments—typically through CI/CD automation, containerization, orchestration, and standardized ML lifecycle tooling.

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 LLM Engineer** builds and improves application features powered by large language models (LLMs), focusing on safe, reliable, and measurable behavior in production. This role contributes to LLM-enabled services such as retrieval-augmented generation (RAG), summarization, classification, extraction, agentic workflows, and conversational interfaces—typically under the guidance of more senior LLM/ML engineers.

The Associate Knowledge Graph Engineer designs, builds, and maintains foundational knowledge graph assets—schemas, pipelines, entity resolution logic, and query interfaces—that connect enterprise data into a semantically consistent graph for AI and ML use cases. This role focuses on delivering reliable graph-ready datasets, improving graph data quality, and enabling downstream applications such as semantic search, recommendations, analytics, and emerging LLM-powered experiences.

The **Associate Generative AI Engineer** builds and improves production-grade generative AI capabilities—typically LLM-powered features such as search augmentation (RAG), summarization, chat assistants, classification/extraction, and agent-like workflows—under the guidance of senior engineers and ML leaders. The role focuses on implementing well-scoped components (prompting, retrieval pipelines, evaluation harnesses, API integration, guardrails, and observability) that make generative AI features reliable, secure, and cost-effective in real software products.

The **Associate Federated Learning Engineer** builds and supports privacy-preserving machine learning systems where model training happens across distributed data sources (e.g., mobile devices, edge nodes, or customer-owned environments) without centralizing raw data. This role contributes to the design, implementation, and evaluation of federated learning (FL) pipelines, focusing on reliable training workflows, secure aggregation patterns, reproducible experiments, and practical integration into product and platform environments.

The Associate Edge AI Engineer designs, optimizes, and deploys machine learning inference workloads on resource-constrained edge devices (e.g., gateways, cameras, industrial PCs, mobile/embedded systems), ensuring models run reliably with low latency, acceptable accuracy, and safe operational behavior. This role bridges applied ML engineering with systems engineering realities—compute limits, memory budgets, thermal constraints, intermittent connectivity, and device lifecycle management.

The Associate Computer Vision Engineer designs, trains, evaluates, and helps deploy computer vision models that turn images and video into product features and operational capabilities. The role focuses on building reliable model pipelines and production-ready inference components under guidance from senior engineers/scientists, while developing strong fundamentals in vision algorithms, deep learning, and MLOps practices.

The Associate Autonomous Systems Engineer contributes to the design, development, testing, and deployment of software components that enable autonomy—systems that perceive their environment, make decisions, and act with limited human intervention. At the associate level, the role focuses on implementing well-scoped modules (e.g., perception preprocessing, localization utilities, planning primitives, simulation tooling) under guidance, while building strong fundamentals in safety, reliability, and real-world performance constraints.

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 **Associate AI Safety Engineer** helps design, implement, test, and operate safety controls that reduce harmful, insecure, non-compliant, or unreliable behavior in AI/ML systems—especially systems using large language models (LLMs), retrieval-augmented generation (RAG), and ML-driven product features. This is an **early-career individual contributor (IC)** engineering role focused on turning Responsible AI principles into concrete technical safeguards, measurable evaluations, and repeatable engineering practices.

The **Associate AI Platform Engineer** helps build, operate, and continuously improve the internal platform capabilities that enable data scientists and ML engineers to train, evaluate, deploy, and monitor machine learning models reliably in production. This role focuses on implementing well-defined components (infrastructure, CI/CD automation, model packaging, deployment workflows, observability hooks, and guardrails) under the guidance of senior engineers, while building strong foundational skills in MLOps and platform engineering.

The Associate AI Evaluation Engineer designs, implements, and operates repeatable evaluation processes that measure the quality, safety, and reliability of AI systems—most commonly large language model (LLM) features, retrieval-augmented generation (RAG) experiences, and classical ML components embedded in software products. The role focuses on building evaluation harnesses, curating test datasets, defining metrics and acceptance criteria, and turning model behavior into actionable engineering and product decisions.

The **Associate AI Engineer** is an early-career engineering role within the **AI & ML** department responsible for building, integrating, testing, and operating AI-enabled software components under the guidance of more senior engineers. The role focuses on turning well-scoped model and data requirements into reliable code, reproducible experiments, and production-ready artifacts (APIs, batch jobs, pipelines, monitoring hooks) that support AI features in products and internal platforms.

The Associate AI Agent Engineer builds, tests, and operates “agentic” AI capabilities—software components that use large language models (LLMs) plus tools, memory, retrieval, and orchestration to complete multi-step tasks reliably inside products and internal workflows. This role focuses on implementing well-scoped agents, improving their accuracy and safety, and integrating them into production services with strong observability and evaluation practices.

The **Applied AI Engineer** designs, builds, and ships AI-driven capabilities into production software systems, turning model prototypes and research outcomes into reliable, observable, secure, and cost-effective product features. The role sits at the intersection of software engineering, machine learning engineering, and product delivery—owning the “last mile” of applied AI: integration, deployment, evaluation, and operational excellence.

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 Safety Engineer** designs, implements, and operates technical safeguards that reduce harm from machine learning (ML) systems—especially modern generative AI and LLM-enabled features—while preserving product usefulness and performance. The role blends software engineering, applied ML evaluation, security-minded threat modeling, and governance-aware delivery to ensure AI systems behave reliably under real-world usage, misuse, and adversarial conditions.

The AI Reliability Engineer ensures that AI/ML-powered products and platforms are dependable in production—meeting reliability, latency, cost, and quality targets while remaining safe and observable under real-world usage. This role blends Site Reliability Engineering (SRE) practices with ML operations realities (non-determinism, data drift, model/version sprawl, and rapidly evolving dependencies).

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.

The **AI Quality Engineer** is responsible for defining, implementing, and operating quality practices for AI/ML-enabled products and platforms—ensuring models, data, and AI-powered features behave reliably, safely, and measurably across real-world conditions. The role blends software quality engineering with ML evaluation, data validation, and production monitoring to prevent regressions, reduce risk, and increase customer trust in AI-driven capabilities.

The **AI Policy Engineer** designs, operationalizes, and enforces responsible AI and AI governance requirements as **technical controls** across the AI/ML lifecycle—turning policy intent (legal, risk, ethics, security, product) into **deployable engineering mechanisms** (policy-as-code, pipeline gates, automated evaluations, documentation automation, and audit-ready evidence). This role exists in software and IT organizations because modern AI systems (especially GenAI) introduce fast-moving risks—privacy, security, safety, bias, IP, regulatory exposure, and brand harm—that cannot be mitigated by documentation alone and must be **engineered into delivery workflows**.

The **AI Platform Reliability Engineer** ensures that the organization’s AI/ML platform (training pipelines, feature/data dependencies, model registry, and online inference/serving) is **reliable, observable, scalable, secure, and cost-effective**. This role applies Site Reliability Engineering (SRE) principles to ML systems, where reliability must account for both classic uptime/latency concerns and ML-specific behaviors like model drift, data quality regressions, and reproducibility.

The **AI Platform Engineer** designs, builds, and operates the internal platform capabilities that enable teams to develop, deploy, and run machine learning (ML) and AI systems reliably in production. This role focuses on creating secure, scalable, developer-friendly “paved roads” for model training, evaluation, deployment, observability, and governance—so product teams and data scientists can deliver AI features faster with less operational risk.

The **AI Guardrails Engineer** designs, builds, and operates technical controls (“guardrails”) that make AI systems safer, more reliable, policy-compliant, and predictable in production. This role focuses on preventing and detecting harmful, insecure, non-compliant, or low-quality AI behavior—especially in **LLM-powered** features, agentic workflows, and AI-assisted user experiences.

The AI Governance Engineer designs, implements, and operates the technical controls that ensure AI/ML systems are safe, compliant, auditable, and aligned with organizational policy throughout their lifecycle—from data intake and model training to deployment, monitoring, and decommissioning. This role sits at the intersection of engineering, risk, and responsible AI, translating governance requirements into automated guardrails, tooling, and repeatable processes that integrate directly into ML and software delivery pipelines.

The **AI Evaluation Engineer** designs, implements, and operates the evaluation systems that determine whether AI/ML (especially LLM-powered) features are *good enough, safe enough, and reliable enough* to ship and to keep running in production. This role turns ambiguous product intent (“make answers more helpful”) into measurable quality targets, repeatable test suites, and release gates that prevent regressions and reduce AI risk.

The AI Engineer designs, builds, deploys, and operates machine-learning–powered capabilities in production software systems. The role bridges applied ML modeling, data engineering, and software engineering to deliver reliable AI features (e.g., personalization, forecasting, classification, retrieval, ranking, and conversational experiences) that meet business, security, and performance requirements.