AI & ML

The **Senior Generative AI Engineer** designs, builds, and operates production-grade generative AI capabilities—typically LLM-powered applications, retrieval-augmented generation (RAG) systems, model-serving APIs, evaluation pipelines, and safety controls—that create measurable product and operational outcomes. This is a **senior individual contributor (IC)** role with end-to-end technical ownership across experimentation, engineering hardening, deployment, and lifecycle operations.

The **Senior Federated Learning Engineer** designs, builds, and operationalizes privacy-preserving machine learning systems that train across distributed data sources (devices, edge nodes, partner environments, or business units) without centralizing raw data. This role exists in software and IT organizations to unlock model performance and product intelligence while meeting rising privacy, data residency, and regulatory constraints.

The Senior Edge AI Engineer designs, optimizes, and operationalizes machine learning (ML) systems that run directly on edge devices (e.g., gateways, cameras, industrial controllers, mobile/embedded compute modules) where low latency, intermittent connectivity, privacy constraints, and hardware limitations shape the solution. This role translates model research and product requirements into production-grade on-device inference pipelines, including model compression, hardware acceleration, deployment automation, and fleet observability.

The Senior Computer Vision Engineer designs, builds, and productionizes computer vision (CV) models and systems that interpret images and video to enable product capabilities such as detection, segmentation, tracking, OCR, and visual similarity. This role exists in a software or IT organization to turn visual data into reliable, scalable, and measurable product outcomes, bridging research-grade modeling with production-grade engineering, MLOps, and runtime optimization.

The **Senior Autonomous Systems Engineer** designs, builds, and validates autonomy capabilities that allow software-driven systems to perceive their environment, make decisions, and act safely with minimal human intervention. This role sits at the intersection of **AI/ML, robotics software, real-time systems, and safety engineering**, translating research-grade autonomy methods into reliable, testable, and deployable production software.

The **Senior Applied AI Engineer** designs, builds, and operates AI-powered product capabilities by turning research-grade approaches into **reliable, secure, scalable, and measurable** production systems. This role sits at the intersection of software engineering, machine learning, and data engineering, with a strong focus on **delivering user and business outcomes** rather than experimentation alone.

The **Senior AI Safety Engineer** designs, implements, and operates technical safeguards that reduce the likelihood and impact of harmful behavior in AI systems (particularly LLM- and agent-enabled products). This role builds evaluation and monitoring systems, integrates guardrails into product and platform workflows, and partners with product, security, privacy, and legal stakeholders to ensure AI capabilities ship responsibly and reliably.

The **Senior AI Platform Engineer** designs, builds, and operates the internal platform capabilities that enable teams to reliably develop, train, evaluate, deploy, and monitor machine learning (ML) and generative AI (GenAI) systems at scale. The role balances strong software engineering and cloud infrastructure skills with pragmatic MLOps practices, focusing on repeatability, security, cost efficiency, and developer experience for data scientists and ML engineers.

The Senior AI Evaluation Engineer designs, implements, and operationalizes robust evaluation systems to measure the quality, safety, reliability, and business performance of AI models—especially modern ML and LLM-based capabilities—throughout the development lifecycle and in production. The role translates ambiguous “model quality” questions into measurable metrics, repeatable test suites, and automated gates that prevent regressions and enable responsible scaling of AI features.

The Senior AI Engineer designs, builds, deploys, and operates production-grade machine learning (ML) and generative AI capabilities that deliver measurable business outcomes in a software or IT organization. This role bridges applied research and software engineering by translating problem statements into reliable model-powered services, data/feature pipelines, evaluation frameworks, and scalable inference architectures.

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 **Robotics Software Engineer** designs, builds, tests, and deploys software that enables robots to perceive their environment, make decisions, and act reliably in the physical world. In a software or IT organization—especially within an **AI & ML** department—this role bridges machine learning, real-time systems, and production-grade software engineering to deliver robotic capabilities as a product, platform, or internal capability.

The **Robotics ML Engineer** designs, trains, evaluates, and deploys machine learning models that enable robots to perceive, predict, and act reliably in real-world environments. The role bridges applied ML engineering with robotics constraints such as real-time performance, safety, edge compute limits, and hardware variability.

A Retrieval Engineer designs, builds, and operates the retrieval layer that selects the best candidate information for downstream AI systems (e.g., RAG applications, search experiences, recommendations, and ranking pipelines). The role focuses on indexing strategies, query understanding, hybrid retrieval (lexical + vector), relevance evaluation, and performance engineering so that the right content is fetched reliably, safely, and at low latency.

The **Responsible AI Engineer** designs, implements, and operationalizes engineering controls that make AI/ML systems **safer, fairer, more transparent, more secure, and more compliant** throughout the model lifecycle—from experimentation to production monitoring. The role bridges applied ML engineering and risk governance by embedding responsible AI requirements into **pipelines, evaluation harnesses, deployment gates, and runtime safeguards**.

A Recommendation Systems Engineer designs, builds, evaluates, and operates machine learning systems that personalize user experiences by predicting and ranking the most relevant content, products, or actions for each user in real time and batch contexts. The role sits at the intersection of software engineering, applied machine learning, and product experimentation—turning behavioral signals and content metadata into reliable, scalable recommendation services.

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 Prompt Optimization Engineer designs, tests, and continuously improves prompts, retrieval strategies, and interaction patterns that drive high-quality outcomes from large language models (LLMs) and related generative AI systems in production software. The role blends applied NLP/LLM engineering, experimentation discipline, and product-quality thinking to reliably convert business intent into precise, safe, and cost-effective model behavior.

The Prompt Engineer designs, tests, and operationalizes prompt- and instruction-based interactions with large language models (LLMs) to deliver reliable, safe, and product-aligned AI features. This role converts product intent and user needs into repeatable prompt patterns, evaluation harnesses, and production-ready prompt configurations that meet quality, security, and cost targets.

The **Principal Synthetic Data Engineer** is a senior individual contributor (IC) responsible for designing, building, and governing enterprise-grade synthetic data capabilities that accelerate AI/ML development while reducing privacy, security, and data access constraints. This role combines deep data engineering and ML knowledge with rigorous privacy/utility evaluation to produce synthetic datasets that are fit-for-purpose for model training, testing, analytics, and product experimentation.

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 **Principal Responsible AI Engineer** is a senior individual contributor who designs, implements, and operationalizes responsible AI (RAI) controls across the end-to-end AI/ML lifecycle—spanning data, training, evaluation, deployment, monitoring, and retirement. This role ensures that AI-enabled products and platforms are **safe, fair, privacy-preserving, secure, explainable where necessary, and governed** in ways that meet internal standards and evolving external expectations.

The **Principal Recommendation Systems Engineer** is a senior individual contributor (IC) responsible for designing, building, and continuously improving large-scale recommendation and personalization systems that drive measurable user and business outcomes (engagement, retention, conversion, satisfaction, and revenue). This role combines deep machine learning expertise with production-grade engineering rigor to deliver low-latency, high-throughput ranking and retrieval services integrated into customer-facing products.

The Principal RAG Engineer is a senior individual contributor responsible for designing, building, and operating Retrieval-Augmented Generation (RAG) systems that deliver reliable, secure, and high-quality AI experiences in production. This role blends applied ML engineering, search/retrieval engineering, distributed systems, and software architecture to ensure LLM-based products are grounded in trusted enterprise knowledge and perform predictably at scale.

The Principal Prompt Engineer is a senior individual-contributor engineering role in the AI & ML organization responsible for designing, standardizing, and operationalizing prompt- and instruction-based interfaces to large language models (LLMs) and multimodal foundation models. This role converts product and business intent into reliable, safe, and cost-effective model behaviors—using prompt systems, retrieval-augmented generation (RAG) patterns, tool/function calling, agent workflows, and evaluation harnesses.

The Principal NLP Engineer is a senior individual contributor (IC) responsible for architecting, building, and operationalizing production-grade natural language processing (NLP) capabilities—often including large language models (LLMs), retrieval-augmented generation (RAG), classic NLP pipelines, and evaluation systems—at enterprise scale. This role translates ambiguous product and platform needs into reliable language intelligence services that are secure, measurable, and maintainable.

The Principal MLOps Engineer is a senior individual contributor responsible for designing, standardizing, and scaling the end-to-end systems that reliably deliver machine learning models into production. This role bridges ML engineering, data engineering, DevOps/SRE, and security to ensure models are deployable, observable, governed, cost-efficient, and continuously improving.

The **Principal Machine Learning Engineer** is a senior individual contributor (IC) responsible for designing, delivering, and operating production-grade machine learning systems that materially improve product outcomes and business performance. This role combines deep applied ML expertise with strong software engineering, architecture, and operational excellence—ensuring models are not only accurate, but also reliable, observable, secure, cost-effective, and maintainable over time.

The Principal LLMOps Engineer designs, builds, and governs the production operating environment for Large Language Model (LLM) capabilities—covering deployment, routing, evaluation, monitoring, safety controls, and lifecycle management across internal and customer-facing applications. The role exists to turn experimental LLM prototypes into reliable, cost-effective, secure, and observable services that can be operated at enterprise scale.

The **Principal LLM Engineer** is a senior individual-contributor engineering leader responsible for designing, building, and scaling large language model (LLM) capabilities that are reliable in production, economically efficient, and aligned with safety, privacy, and product requirements. This role turns LLM research advances and vendor offerings into **repeatable platform capabilities** (e.g., RAG, evaluation, guardrails, routing, fine-tuning, observability) that product and engineering teams can safely and rapidly adopt.