Search Results for "Jenkins"

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 Knowledge Graph Engineer designs, builds, and operationalizes enterprise-grade knowledge graph capabilities that connect data, concepts, and relationships to power AI-driven experiences such as search, recommendations, analytics, and agentic workflows. This role blends deep graph engineering, semantic modeling, and production software engineering to deliver a governed, performant, and evolvable “knowledge layer” across products and internal platforms.

The **Principal Edge AI Engineer** is a senior individual contributor (IC) responsible for architecting, delivering, and operationalizing **machine learning inference and intelligent decisioning on edge devices** (e.g., gateways, industrial PCs, retail devices, mobile/embedded endpoints) where constraints such as latency, connectivity, privacy, power, and cost materially shape the solution. This role designs the end-to-end edge AI “production system”: model packaging and optimization, device runtime architecture, secure deployment and updates, observability, and continuous improvement loops.

The Principal Autonomous Systems Engineer is a senior individual-contributor (IC) engineering role responsible for designing, validating, and scaling autonomy capabilities (perception, prediction, planning, control, and autonomy orchestration) that operate reliably in complex, real-world environments. This role blends advanced software engineering, applied ML, systems architecture, and safety-minded engineering to deliver end-to-end autonomous behaviors that meet product requirements and operational constraints.

The **Principal AI Platform Engineer** is a senior individual-contributor (IC) engineering leader responsible for designing, building, and evolving the internal platform capabilities that enable teams to develop, deploy, operate, and govern machine learning (ML) and generative AI solutions safely and efficiently at enterprise scale. This role unifies **platform engineering**, **MLOps**, **LLMOps**, **reliability engineering**, and **AI governance-by-design** into a coherent “paved road” that accelerates delivery while reducing operational and compliance risk.

The **Principal AI Evaluation Engineer** designs, implements, and governs the evaluation systems that determine whether AI models (including LLMs and traditional ML) are *safe, effective, reliable, and fit for production use*. This role establishes enterprise-grade evaluation methodology—offline benchmarks, online experimentation, human-in-the-loop scoring, and continuous monitoring—to reduce model risk and accelerate high-confidence releases.

The **Principal AI Engineer** is a senior, hands-on technical leader responsible for designing, building, and operating production-grade AI/ML (including GenAI where applicable) capabilities that materially improve product outcomes, internal productivity, and platform differentiation. This role bridges applied machine learning, software engineering, and reliable operations—ensuring models and AI services are safe, scalable, measurable, and maintainable.

The **Multi-Agent Systems Engineer** designs, builds, and operates software systems where multiple AI agents (often LLM-powered) coordinate to accomplish complex workflows—planning, tool use, delegation, verification, and iterative improvement—within production-grade applications. The role blends applied machine learning, distributed systems thinking, and product engineering to turn agent research patterns into reliable, secure, cost-effective capabilities.

The **Model Validation Engineer** is an individual contributor engineering role responsible for independently assessing, testing, and challenging machine learning (ML) models before and after deployment to ensure they are accurate, robust, explainable, and safe for production use. This role designs and executes validation methodologies (offline evaluation, bias/fairness checks, stress testing, drift monitoring, and reproducibility verification) and translates findings into actionable engineering and product decisions.

A **Model Operations Engineer** designs, builds, and runs the production-grade systems and operating practices that allow machine learning (ML) models to be deployed safely, monitored continuously, and improved reliably over time. The role sits at the intersection of software engineering, platform operations, and applied ML—translating data science outputs into **durable, observable, compliant** services that deliver business value in real products.

The Model Evaluation Engineer designs, implements, and operationalizes how machine learning (ML) and AI models are measured, compared, validated, and continuously monitored across their lifecycle—from offline experimentation to production performance and safety. The role exists to ensure model quality is not anecdotal or ad hoc, but governed by repeatable evaluation methods, reliable datasets, robust metrics, and automated test harnesses that prevent regressions and support trustworthy releases.

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 Machine Learning Engineer (MLE) designs, builds, deploys, and operates machine learning systems that deliver measurable product and business outcomes in a production software environment. This role bridges data science and software engineering by turning models and experimentation into reliable, observable, secure, and scalable services and pipelines.

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.