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Llm Analyst Jobs (NOW HIRING)

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LLM Applications Engineer

New York, NY · On-site

$130K - $175K/yr

Build intuitive, AI-driven UI components that allow scientists to query, analyze, and visualize complex experimental datasets. * Integration: Connect LLM capabilities with our core SQL databases and ...

Luminary Cloud

LLM Engineer

San Mateo, CA · On-site

Connect LLM capabilities with Luminary's Physics AI training/evaluation/inference pipelines, physics simulation solvers, mesh tools, and analytics APIs to enable end-to-end automation * Establish ...

Siemens Healthineers

AI LLM Engineer

Atlanta, GA · On-site +1

AI / LLM Engineering & Agentic Systems * Design, build, and deploy LLM powered applications using ... Work across business, analytics, and engineering teams to identify high impact AI use cases

Apple

Machine Learning Engineer - LLM

Cupertino, CA · On-site

Ability to meaningfully present results of analyses in a clear and impactful manner, breaking down complex ML/LLM concepts for non-technical audiences.Proven experience in leading and mentoring teams.

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Llm Analyst information

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$31K

$73.3K

$130K

How much do llm analyst jobs pay per year?

As of Jun 10, 2026, the average yearly pay for llm analyst in the United States is $73,261.00, according to ZipRecruiter salary data. Most workers in this role earn between $52,500.00 and $87,000.00 per year, depending on experience, location, and employer.

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

To thrive as an LLM Analyst, you need a solid background in data science, natural language processing (NLP), and machine learning, often supported by a relevant degree in computer science or a related field. Familiarity with tools and frameworks like Python, TensorFlow, PyTorch, and experience using large language models (LLMs) are typically required. Strong analytical thinking, attention to detail, and effective communication skills help in interpreting results and collaborating with cross-functional teams. These skills are crucial for developing, evaluating, and optimizing LLM applications to deliver accurate and impactful AI solutions.

What is the difference between Llm Analyst vs Data Scientist?

AspectLlm AnalystData Scientist
Required CredentialsBachelor's in Computer Science, Data Science, or related field; knowledge of machine learning and NLPBachelor's or Master's in Data Science, Statistics, or related; strong programming and statistical skills
Work EnvironmentTech companies, AI firms, research labs focusing on language modelsVarious industries including tech, finance, healthcare; data-driven roles
Employer & Industry UsagePrimarily in AI and NLP-focused companiesBroadly across industries with data analysis needs

While both roles involve working with data and machine learning, an Llm Analyst specializes in language models and NLP applications, whereas a Data Scientist has a broader focus on data analysis, modeling, and insights across various domains.

What are some common challenges faced by LLM Analysts when working with large language models in a production environment?

LLM Analysts often encounter challenges such as optimizing model performance while balancing computational costs, ensuring data privacy and compliance, and troubleshooting unexpected model outputs. Collaborating closely with data engineers and machine learning researchers is essential to address issues like data pipeline bottlenecks and model drift. Additionally, LLM Analysts must continuously monitor and retrain models to maintain accuracy, which requires strong analytical and problem-solving skills in a fast-paced, collaborative environment.

What is an LLM Analyst?

An LLM Analyst is a professional who specializes in working with large language models (LLMs), such as GPT or similar AI systems. Their role typically involves evaluating, fine-tuning, and analyzing the performance of these models for specific business or research applications. LLM Analysts may also handle tasks like prompt engineering, data annotation, and quality assurance to ensure that the language model meets designated objectives and safety standards. This position requires a strong understanding of machine learning concepts, natural language processing (NLP), and data analysis.
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What cities are hiring for Llm Analyst jobs? Cities with the most Llm Analyst job openings:
What states have the most Llm Analyst jobs? States with the most job openings for Llm Analyst jobs include:
What job categories do people searching Llm Analyst jobs look for? The top searched job categories for Llm Analyst jobs are:
Llm Analyst jobs near you
Infographic showing various Llm Analyst job openings in the United States as of June 2026, with employment types broken down into 4% Locum Tenens, 64% Full Time, 18% Part Time, and 14% Contract. Highlights an 77% Physical, 5% Hybrid, and 18% Remote job distribution, with an average salary of $73,261 per year, or $35.2 per hour.
AI & LLM Infrastructure FinOps Analyst

AI & LLM Infrastructure FinOps Analyst

Bloomberg LP

New York, NY • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 9 days ago

Job description

AI & LLM Infrastructure FinOps Analyst
Location
New York
Business Area
Engineering and CTO
Ref #
10050218
Description & Requirements
Overview
We are seeking a highly technical FinOps leader to own cost architecture, optimization, and financial observability across our AI and LLM platforms. This role will operate at the intersection ML engineering, cloud infrastructure and finance, with deep involvement in model selection, inference optimization, GPU utilization, and provisioned throughput strategy.
You will partner closely with Engineering, AI/ML Platform, and Finance teams to implement reporting frameworks that enable informed decision-making, optimize resource allocation, and establish sustainable cost models.
You will build cost transparency into the AI stack itself - from token-level economics through GPU cluster utilization - and partner directly with engineering teams to design for cost-efficiency at scale.
AI costs scale non-linearly with usage. As we expand our LLM-powered products, disciplined financial management, throughput optimization, and transparent reporting will be critical to ensuring sustainable growth.
Key Responsibilities
AI & LLM Cost Governance
  • Develop and maintain dashboards/cost models for all AI/LLM-related infrastructure.
  • Implement chargeback/showback models across business units.
  • Build cost allocation pipelines integrating cloud billing exports into internal data warehouses.
  • Oversight of LLM-related spend (API usage, hosted models, self-hosted models, inference endpoints).
  • Help define unit economics for AI usage (cost per request, per workflow, per customer, etc.).
  • Deliver monthly executive reporting with actionable insights.
  • Develop forecasting models tied to product adoption and growth.

Provisioned Throughput & Capacity Optimization
  • Vendor Coordination
  • Optimize usage of provisioned throughput across all providers.
  • Forecast demand and align capacity planning with engineering roadmaps.
  • Analyze idle capacity, overprovisioning, and burst patterns.
  • Evaluate trade-offs between on-demand vs. reserved capacity vs. self-hosted models.
  • Partner with Engineering and CTO to right-size model selection and inference configurations.

Cost Optimization & Performance Trade-offs
  • Identify cost-saving opportunities through working with the AI Infrastructure teams
  • Work to balance latency, quality, and cost.
  • Monitor and report on cost anomalies and usage spikes.
  • Determine effective cost per inference

Tooling & Automation
  • Implement/manage FinOps tooling for AI/LLM's in alignment with current FinOps team resources
  • Build automated cost pipelines using:

-Cloud billing exports (AWS CUR, Azure Cost Management, GCP Billing)
-SQL / Python-based transformations
-BI tools (e.g., QlikSense)
  • Help build automated tagging and allocation frameworks.
  • Establish anomaly detection and spend guardrails.
  • Standardize metrics across multi-cloud and multi-model environments.
  • Integrate cost telemetry into existing tooling.

Required Qualifications
  • 5+ years in FinOps, cloud financial management, or technical finance.
  • Direct experience managing cloud infrastructure spend (AWS, Azure, GCP).
  • Experience with Azure OpenAI, OpenAI API, Anthropic, or similar platform consoles.
  • Experience working with AI/ML or LLM-based workloads.
  • Strong understanding of:

-AI platform engineering
-LLM pricing mechanics (token billing, context windows)
-GPU infrastructure economics
  • Provisioned throughput / reserved capacity
  • Cloud commitment strategies
  • Kubernetes-based ML workloads
  • Cloud billing exports and APIs
  • Experience building forecasting and financial models for variable usage systems.
  • Experience embedding FinOps practices within engineering teams.
  • Strong analytical skills (SQL, Python, Excel/Sheets, BI tools).
  • Ability to interpret GPU utilization, inference latency, and throughput metrics.
  • Understanding of inference optimization techniques.
  • Ability to communicate complex cost structures to technical and non-technical stakeholders.
  • A Degree in Computer Science, Engineering, Mathematics, similar field of study or equivalent work experience

Salary Range = 160,000 - 220,000 USD Annual + Benefits + Bonus
The referenced salary range is based on the Company's good faith belief at the time of posting. Actual compensation may vary based on factors such as geographic location, work experience, market conditions, education/training and skill level.
We offer one of the most comprehensive and generous benefits plans available and offer a range of total rewards that may include merit increases, incentive compensation (exempt roles only), paid holidays, paid time off, medical, dental, vision, short and long term disability benefits, 401(k) +match, life insurance, and various wellness programs, among others. The Company does not provide benefits directly to contingent workers/contractors and interns.
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About Bloomberg

Sourced by ZipRecruiter

Bloomberg runs on data. As the Data Management & Analytics team within Engineering, we support our organization's needs around managing data efficiently. The vision of the team is to build solutions that drive data quality, data dictionary, data stewardship, data lineage, reference, and master data management across various data domains (prospect, customer, vendor, material etc.). We partner with business teams across the organization in addressing their data needs and ultimately helping run business operations efficiently and make improved decisions.

Industry

Finance and insurance

Company size

10,000+ Employees

Headquarters location

New York, NY, US

Year founded

1981

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