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Llm Delivery Jobs (NOW HIRING)

C the Signs

Senior MLOps Engineer

$107K - $146K/yr

Build and own end-to-end LLM delivery pipelines: prompt/versioning, retrieval, orchestration, evaluation, deployment, monitoring, and iterative improvement. * Create robust LLM evaluation harnesses ...

Luminary Cloud

LLM Engineer

San Mateo, CA · On-site

Work closely with Physics AI researchers, platform engineers, and product teams to deliver customer ... Deep understanding of LLM architectures, prompting techniques, and their capabilities/limitations

Siemens Healthineers

AI LLM Engineer

Atlanta, GA · On-site +1

AI / LLM Engineering & Agentic Systems * Design, build, and deploy LLM powered applications using ... Integrate with Power BI/Fabric to deliver solutions with NL querying and automated insights ...

Minfy

Senior AI / LLM Engineer

Mclean, VA · On-site

$107K - $147K/yr

Own the design and delivery of LLM-powered features end-to-end -- from problem framing and architecture through production deployment and iteration. • Build and tune retrieval-augmented generation ...

Horizon3.ai

Staff Attack Engineer, AI/LLM

Our flagship product, the NodeZeroTM platform, delivers production-safe autonomous pentests and ... Essential Functions Attacking AI/LLM Systems * Break AI and agentic systems and translate that ...

Gap International

AI/LLM Engineer

Springfield, PA

As part of our growth strategy, we're expanding our AI capabilities to deliver cutting-edge ... As an AI/LLM Engineer, you will lead the design and implementation of advanced systems centered on ...

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Llm Delivery information

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$13

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$91

How much do llm delivery jobs pay per hour?

As of Jul 3, 2026, the average hourly pay for llm delivery in the United States is $46.36, according to ZipRecruiter salary data. Most workers in this role earn between $20.43 and $60.58 per hour, depending on experience, location, and employer.

What is the difference between Llm Delivery vs Data Scientist?

AspectLlm DeliveryData Scientist
Required CredentialsTypically requires knowledge of AI/ML deployment, cloud platforms, and programming skillsRequires degrees in data science, statistics, or related fields, with skills in programming and analytics
Work EnvironmentOften involves collaboration with AI teams, cloud infrastructure, and client-facing projectsWorks with data analysis, modeling, and visualization within teams or independently
Employer & Industry UsageUsed in tech companies, AI service providers, and consulting firmsCommon in tech, finance, healthcare, and research organizations
Search & Comparison IntentPeople compare roles related to AI deployment and implementationPeople compare roles focused on data analysis and modeling

While both roles involve technical skills, Llm Delivery focuses on deploying large language models and AI solutions, whereas Data Scientists primarily analyze data and build predictive models. Understanding these differences helps candidates choose the right career path or job opportunity.

What are some common challenges faced by professionals in LLM Delivery roles, and how can they be addressed?

Professionals in LLM Delivery often encounter challenges such as aligning large language model solutions with client requirements, managing cross-functional teams, and ensuring robust model deployment and monitoring. Successfully navigating these challenges typically involves clear communication with stakeholders, staying updated on best practices in AI model deployment, and collaborating closely with data scientists, engineers, and product managers. Building strong project management skills and fostering a culture of continuous feedback can also help in delivering high-quality, scalable LLM solutions.

Does Amazon use third party delivery drivers?

Amazon employs both its own delivery network, Amazon Logistics, and third-party delivery drivers through partnerships with companies like UPS, FedEx, and independent contractors via programs such as Amazon Flex. Delivery drivers, including those working for Amazon Flex, are often classified as independent contractors and need a valid driver's license and a reliable vehicle. The use of third-party drivers varies by location and delivery type, with some deliveries handled by Amazon employees and others by external contractors.

What jobs make $1,000,000 a year?

In the field of Llm Delivery or related AI and technology roles, earning $1,000,000 annually is rare and typically limited to top executives, founders, or highly successful entrepreneurs in tech companies. High-level positions such as CTOs or CEOs in large organizations or startups with significant revenue may reach this income level, often supplemented by stock options or equity. Most professionals in this field do not reach this income threshold without substantial business success or ownership stakes.

What is a LLM in a career?

In a career context, LLM typically refers to a Master of Laws degree, a postgraduate qualification in law that can enhance legal expertise and career prospects. It is often pursued by legal professionals seeking specialization or advanced knowledge in areas such as international law, corporate law, or intellectual property. The term is also used in technology to denote large language models, but in a job context, it most commonly relates to the law degree.

What is an LLM Delivery specialist?

An LLM Delivery specialist is a professional responsible for deploying, integrating, and maintaining large language models (LLMs) within an organization or for clients. Their work involves overseeing the effective implementation of LLM solutions, ensuring they meet business requirements, and handling issues like scalability, data privacy, and performance optimization. They often collaborate with data scientists, engineers, and stakeholders to deliver AI-driven applications and services powered by LLMs.

What is the highest paid delivery job?

In the delivery industry, roles such as specialized courier or logistics manager tend to be among the highest paid, often earning over $70,000 annually. These positions typically require experience, advanced logistics skills, and sometimes certifications, and may involve managing large delivery fleets or operating in high-demand sectors.

What are the key skills and qualifications needed to thrive as an LLM Delivery specialist, and why are they important?

To thrive as an LLM Delivery specialist, you need a strong background in machine learning, natural language processing, and software engineering, often supported by a degree in computer science or a related field. Familiarity with large language model frameworks (such as OpenAI, Hugging Face), cloud platforms, and MLOps tools is typically required, along with experience in model deployment and monitoring. Excellent problem-solving skills, effective communication, and adaptability are vital soft skills for collaborating with cross-functional teams and addressing client needs. These competencies ensure successful implementation, scalability, and optimization of language model solutions in dynamic production environments.
More about Llm Delivery jobs
What cities are hiring for Llm Delivery jobs? Cities with the most Llm Delivery job openings:
What states have the most Llm Delivery jobs? States with the most job openings for Llm Delivery jobs include:
What job categories do people searching Llm Delivery jobs look for? The top searched job categories for Llm Delivery jobs are:
Llm Delivery jobs near you
Infographic showing various Llm Delivery job openings in the United States as of June 2026, with employment types broken down into 2% As Needed, 4% Full Time, 83% Part Time, and 11% Contract. Highlights an 95% Physical, 2% Hybrid, and 3% Remote job distribution, with an average salary of $96,421 per year, or $46.4 per hour.

Senior MLOps Engineer

C the Signs

Remote

$107K - $146K/yr

Full-time

Posted 2 days ago

Job description

Position Summary
We're hiring a Senior MLOps Engineer with deep machine learning engineering experience to build and operate the production platform powering ML/LLM-driven healthcare workflows. You'll design reliable, secure, and compliant systems for model development, evaluation, deployment, monitoring, and continuous improvement-working closely with ML, data, security, and product teams.
This role is ideal for someone who has shipped ML systems in production and is excited about LLM orchestration, RAG, evaluations, guardrails, and observability in a regulated environment.
Key responsibilities
MLOps & ML Platform
  • Design and operate ML platforms that support end-to-end workflows: data ingestion, feature engineering, training, evaluation, deployment, and monitoring.
  • Build and maintain CI/CD for ML (testing, packaging, versioning, reproducibility, automated rollbacks, approvals).
  • Implement MLOps best practices: model registry, experiment tracking, lineage, governance, and reproducible training environments.
  • Develop scalable training infrastructure (distributed training, GPU scheduling, cost controls, auto-scaling).
  • Create and maintain feature pipelines / feature stores, ensuring consistency between training and inference (training-serving skew prevention).
  • Establish model monitoring and observability: performance, drift, bias/fairness signals (where relevant), latency, throughput, and data quality.
  • Build and own end-to-end LLM delivery pipelines: prompt/versioning, retrieval, orchestration, evaluation, deployment, monitoring, and iterative improvement.
  • Create robust LLM evaluation harnesses (offline + online): golden datasets, automated regression testing, human-in-the-loop review workflows, and risk scoring.
  • Build cost controls: token/cost budgeting, caching strategies, autoscaling, and performance tuning.
Deployment, reliability, and operations
  • Productionize ML Models on GCP using containers and orchestration (e.g., GKE, Cloud Run), and build CI/CD for ML/LLM systems with automated tests and safe rollouts.
  • Implement observability: tracing, metrics, logs, dashboards, alerting for model/system health (latency, token usage, error rates, retrieval quality, hallucination indicators, drift where relevant).
  • Build cost controls: token/cost budgeting, caching strategies, autoscaling, and performance tuning.
Data, governance, and compliance (Healthcare)
  • Design systems with security and privacy by default: IAM, least privilege, secrets management, audit logs, encryption, data retention, and PHI/PII handling.
  • Implement governance: model/prompt lineage, dataset provenance, evaluation traceability, and approval workflows aligned with healthcare compliance expectations.

Integrate guardrails: content filters, policy checks, prompt injection defenses, structured output validation, and fallback strategies.
Requirements
  • 6+ years in software/platform engineering, including 4+ years operating ML systems in production (or equivalent depth).
  • Strong experience in ML engineering: training pipelines, evaluation, deployment patterns, monitoring, and iteration loops.
  • Strong engineering skills in Python, plus production-grade experience building APIs/services.
  • Demonstrated hands-on experience with LLM systems in production and ML engineering: training pipelines, evaluation, deployment patterns, monitoring, and iteration loops.
  • Strong experience with GCP services and cloud-native patterns.
  • Experience with Vertex AI (pipelines, endpoints, feature store, model registry, evaluation) and/or managed vector search on GCP.
  • Experience with containerization and orchestration (Docker, Kubernetes/GKE and/or Cloud Run).

Benefits
Why Join Us?
Joining C the Signs is not just about building AI; it's about shaping the future of healthcare. If you are a technical leader with an unshakable belief in the power of AI to save lives and the ability to make it happen at scale, this is your opportunity to create a tangible, global impact.
Benefits:
  • Competitive salary and benefits package.
  • Flexible working arrangements (remote or hybrid options available).
  • The opportunity to work on life-changing AI technology that directly impacts patient outcomes.
  • Join a team that combines cutting-edge innovation with a mission to save lives and improve health equity.
  • Continuous learning opportunities with access to the latest tools and advancements in AI and healthcare.

Apply