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Python Llm Jobs in New York (NOW HIRING)

Tata Consultancy Service Limited

Engineer

Edison, NJ · On-site

$85K - $91K/yr

Working experience in Data Processing, AI enabled workflows using python * Knowledge of LLM, Prompt Engineering, RAG Architecture, Agentic AI. * Basic knowledge of Hybrid prompting technique

Prophet

Senior Product Manager - AI Foundry Team

New York, NY

$138.40K - $182.70K/yr

Comfort with Python, LLM tooling, agent frameworks, APIs, and data schemas is expected. * AI Evaluation, Quality & Observability - Define how MAIA capabilities are evaluated before and after launch.

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Braintrust

Open Source Engineer - Python

New York, NY · On-site +1

Build elegant, idiomatic, and resilient SDKs that power Braintrust's LLM evaluation and AI ... Have deep expertise in Python and understand what it takes to build fast, idiomatic, and reliable ...

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What is a Python LLM job?

A Python LLM job involves working with Large Language Models (LLMs) using Python to develop, fine-tune, and deploy AI models. Responsibilities may include data preprocessing, prompt engineering, model optimization, and integration with applications. Professionals in this role often work with frameworks like TensorFlow, PyTorch, or Hugging Face Transformers. They may also contribute to improving model efficiency, reducing bias, and ensuring ethical AI usage.

What are the key skills and qualifications needed to thrive in the Python Llm position, and why are they important?

To excel as a Python LLM (Large Language Model) Engineer, you need strong skills in Python programming, machine learning, and natural language processing, typically supported by a degree in computer science or a related field. Proficiency with libraries such as TensorFlow, PyTorch, Hugging Face Transformers, and experience with model deployment platforms are often essential, alongside certifications in AI or data science. Effective communication, problem-solving abilities, and collaboration are important soft skills for working in interdisciplinary teams and delivering results in dynamic environments. These skills ensure the development, fine-tuning, and deployment of advanced language models that meet both technical and business objectives.

What are some common challenges faced by Python LLM Engineers in their daily work?

Python LLM Engineers often encounter challenges related to optimizing model performance, managing large datasets, and adapting models to specific business needs. Working with large-scale language models requires balancing computational resource limitations with the need for high accuracy and efficiency. Collaboration with data scientists, product managers, and DevOps engineers is routine to ensure seamless model integration and deployment. Staying updated on the latest advancements in NLP and continuously improving models based on user feedback are also important aspects of the role.
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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 27 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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