Search Results for: Jenkins

The **Junior Autonomous Systems Engineer** builds and validates software components that enable machines or software agents to perceive their environment, make decisions, and act safely with minimal human intervention. This role contributes to an autonomy stack (e.g., perception, localization, planning, control, or orchestration) and supports the engineering practices required to deliver reliable autonomous behavior in production-like environments.

The **Junior Applied AI Engineer** is an early-career individual contributor who helps design, build, test, and ship machine learning–enabled features into production software systems under the guidance of senior engineers and applied scientists. The role focuses on **applied implementation**: turning validated modeling approaches into reliable, observable services, pipelines, and product experiences.

The Junior AI Evaluation Engineer designs, runs, and maintains repeatable evaluation processes that measure the quality, safety, and reliability of AI/ML systems—especially modern LLM-enabled features—before and after release. The role focuses on turning ambiguous “is it good?” questions into measurable metrics, representative test sets, and automated evaluation pipelines that product and engineering teams can trust.

The **Junior AI Agent Engineer** designs, implements, and iterates on AI “agents” that can plan, call tools, retrieve knowledge, and complete workflows reliably within a software product or internal platform. This role focuses on building **production-grade agent behavior** (prompting, tool interfaces, retrieval pipelines, evaluation harnesses, and guardrails) under the guidance of senior engineers and applied ML leads.

The Federated Learning Engineer designs, builds, and operates privacy-preserving machine learning systems that train models across distributed data sources without centralizing sensitive data. This role exists in software and IT organizations that need to learn from data located on user devices, customer environments, partner organizations, or regulated data stores where direct pooling is constrained by privacy, security, contractual, or residency requirements. The business value is enabling higher-quality models, broader data coverage, and faster model iteration while reducing privacy risk and improving compliance posture.

The **Edge AI Engineer** designs, optimizes, and deploys machine learning inference capabilities to run reliably on **resource-constrained edge environments** such as mobile devices, embedded systems, IoT gateways, industrial PCs, retail kiosks, and on-prem appliances. The role bridges applied ML engineering and systems engineering: it turns trained models into **production-grade, measurable, secure, and maintainable** edge inference solutions.

The **Distinguished Machine Learning Engineer** is a top-tier individual contributor (IC) responsible for setting the technical direction and engineering standards for production-grade machine learning (ML) systems across an organization. This role designs and evolves the end-to-end ML engineering ecosystem—spanning data/feature pipelines, model development, deployment, observability, reliability, and governance—while delivering material business outcomes through scalable, secure, and maintainable ML capabilities.

The **Distinguished LLM Engineer** is a top-tier individual contributor (IC) role responsible for architecting, proving, and operationalizing large language model (LLM) capabilities that measurably improve product value, developer velocity, and business outcomes. This role combines deep hands-on engineering with organization-wide technical leadership—setting standards for model quality, evaluation, safety, performance, and cost efficiency across LLM-powered systems.

The **Distinguished AI Engineer** is a top-tier individual contributor (IC) engineering role responsible for **enterprise-scale technical direction and delivery of AI/ML systems** that materially shape the company’s products, platforms, and operating model. This role combines deep hands-on engineering capability with cross-organization technical leadership to ensure AI solutions are **reliable, secure, cost-effective, governable, and production-grade**.

The **Autonomous Systems Safety Engineer** ensures that autonomy-enabled products (e.g., robotic platforms, autonomous agents, autonomy SDKs, or decision-making services) are designed, verified, and operated with **demonstrable, auditable safety assurances** appropriate to their operational context. This role translates safety intent into actionable engineering requirements, verification evidence, runtime guardrails, and release gates—especially where machine learning and probabilistic behavior complicate traditional assurance methods.

The **Autonomous Systems Engineer** designs, builds, and operationalizes software components that enable systems to perceive, decide, and act with minimal human intervention—reliably, safely, and measurably. In a software company or IT organization, this role typically sits within **AI & ML Engineering** and bridges ML models with real-time systems engineering to deliver autonomy capabilities into products, platforms, or internal operational tooling.

The **Associate Synthetic Data Engineer** designs, builds, and operates early-stage pipelines and tooling to generate **high-utility, privacy-preserving synthetic datasets** that can be safely used for analytics, software testing, and machine learning model development. This role sits at the intersection of data engineering and applied ML, focusing on turning sensitive or scarce real-world data into governed synthetic alternatives with measurable quality and risk characteristics.

The **Associate Robotics Software Engineer** designs, implements, tests, and supports software components that enable robots and robotic systems to perceive, plan, and act reliably in real-world environments. This role sits at the intersection of **robotics engineering** and **AI/ML-enabled autonomy**, typically contributing to production-grade codebases (often ROS/ROS 2-based), simulation workflows, and on-robot integration under the guidance of senior engineers.

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 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 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 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 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 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.