Search Results for "Jenkins"

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 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 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 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.

The Lead Applied AI Engineer designs, builds, and operates production-grade AI systems that deliver measurable product or operational outcomes, with a focus on reliable deployment, monitoring, iteration, and governance. This is a senior individual contributor (IC) leadership role that bridges data science, software engineering, and product delivery to turn models (including ML and LLM-based systems) into scalable, secure, and maintainable capabilities.

The Lead AI Platform Engineer designs, builds, and runs the internal platform capabilities that enable data scientists and software engineers to develop, deploy, monitor, and govern machine learning (ML) and generative AI (GenAI) solutions reliably at scale. This role combines deep platform engineering with ML systems knowledge (MLOps/LLMOps), ensuring that model delivery is secure, repeatable, observable, cost-effective, and aligned with product needs.

The **Lead AI Engineer** designs, builds, and operates production-grade AI/ML systems that deliver measurable product and business outcomes. This role combines deep hands-on engineering (model development, evaluation, deployment, and MLOps) with technical leadership (architecture decisions, standards, mentoring, and cross-functional alignment) to ensure AI solutions are scalable, reliable, secure, and maintainable.

The Lead AI Agent Engineer designs, builds, and operationalizes AI agent systems that can plan, reason over context, call tools/APIs, and safely execute multi-step workflows within enterprise software products and internal platforms. This role sits at the intersection of LLM application engineering, distributed systems, MLOps/LLMOps, and product delivery, translating business workflows into reliable agentic capabilities with measurable outcomes.

A **Knowledge Systems Engineer** designs, builds, and operates the technical systems that transform dispersed organizational information into **reliable, searchable, governable, and AI-ready knowledge**. In an AI & ML department, this role enables high-quality retrieval and reasoning for applications such as enterprise search, support automation, copilots, and RAG (retrieval-augmented generation) workflows by engineering the pipelines, storage, metadata, and evaluation needed for trustworthy knowledge access.

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 Robotics Software Engineer** builds, tests, and maintains software components that enable robots to perceive their environment, make decisions, and execute motion safely and reliably. The role focuses on implementing well-scoped features, fixing defects, improving test coverage, and contributing to a robotics software stack (often ROS 2-based) under the guidance of senior engineers and technical leads.

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.

A **Junior MLOps Engineer** supports the reliable deployment, operation, and continuous improvement of machine learning (ML) systems in production. This role focuses on implementing and maintaining ML delivery pipelines, model packaging and deployment workflows, monitoring and alerting, and the operational hygiene needed to run ML-enabled features as dependable software.

The **Junior Machine Learning Engineer** builds, validates, and deploys machine learning components that power product features and internal decisioning systems. The role focuses on implementing well-scoped ML solutions under guidance, contributing production-quality code, and supporting model lifecycle operations (training, evaluation, deployment, monitoring, and iteration).

The Junior LLM Engineer builds, evaluates, and improves large language model (LLM) features that power customer-facing and internal AI capabilities in a software or IT organization. This role focuses on implementing well-scoped LLM components (prompting, retrieval-augmented generation, evaluation harnesses, safety checks, and integration code) under the guidance of senior engineers and applied scientists.

The **Junior Knowledge Graph Engineer** designs, builds, and maintains foundational components of a knowledge graph system—turning messy enterprise data into connected entities, relationships, and graph-powered features that support AI/ML use cases (search, recommendations, question answering, entity resolution, analytics). This is an **individual contributor (IC)** engineering role with a learning-oriented scope, typically working under guidance from a Senior/Staff Knowledge Graph Engineer or an ML/AI Engineering Manager.

The **Junior Federated Learning Engineer** builds, tests, and operates early-stage federated learning (FL) capabilities that enable machine learning models to be trained across distributed devices or data silos **without centralizing raw data**. This role focuses on implementing training workflows, data and model interfaces, privacy-preserving techniques, and evaluation methods under guidance from senior engineers and applied scientists.

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.

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.