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Llm Ai Jobs (NOW HIRING)

De Circle

Rengo AI - AI Engineer

New York, NY · On-site +1

$125K - $150K/yr

Rengo AI is building the intelligence layer for fund management - starting with next-generation ... Design LLM pipelines that: * avoid hallucinated financial reasoning * produce structured ...

AIToolboard

AI Engineer Senior Level

Manhattan, NY · On-site

$140 - $180/hr

Key Responsibilities- Design and develop LLM-powered AI agents for enterprise workflow automation.- Build integrations between AI agents and internal enterprise systems using:- REST APIs- SQL ...

Photon

Lead AI Engineer

Newark, DE · On-site

$100K - $132K/yr

... Lead AI Engineer Newark, DE - Onsite Responsibilities Owns end-to-end delivery of how the ... Own delivery of LLM API integration and SDK patterns used across applications. * Set organizational ...

Tenex.AI Inc

Principal Product Manager - AI

Deep LLM & AI Knowledge: Strong understanding of how LLMs, multi-agent architectures, and RAG pipelines can be leveraged to automate security analyst workflows. * Cybersecurity Expertise: Deep domain ...

Northern Base

AI/LLM Engineer

New York, NY · On-site

Hiring Alert | AI/LLM Engineer Location: Edison, NY (Hybrid - 3 Days Onsite) Employment Type: Full-Time Experience Required: 8-10 Years Visa Type: USC / GC Only Interview Mode: In-person (Final Round ...

New

Horizon3.ai

Staff Attack Engineer, AI/LLM

Essential Functions Attacking AI/LLM Systems * Break AI and agentic systems and translate that research into automated, repeatable attack modules for NodeZero. * Design and execute prompt injection ...

acralending

AI Engineer

Irvine, CA · On-site

LLM & AI Engineering * Implement Retrieval-Augmented Generation (RAG) systems using lending guidelines, overlays, investor matrices, and SOPs. * Build prompt orchestration, memory systems, and agent ...

acralending

AI Engineer

Irvine, CA · On-site

LLM & AI Engineering * Implement Retrieval-Augmented Generation (RAG) systems using lending guidelines, overlays, investor matrices, and SOPs. * Build prompt orchestration, memory systems, and agent ...

Beacon Systems, Inc

Senior Data Architect

Bernardsville, NJ · On-site

$69.50 - $93/hr

Key Responsibilities: 1. LLM & AI Agent Architecture * Design and implement LLM-enabled architectures, including RAG * (Retrieval-Augmented Generation) solutions using structured and unstructured ...

Autonomize AI

ML/LLM Engineer - Applied AI

Austin, TX · On-site

Autonomize AI is revolutionizing healthcare by streamlining knowledge workflows with AI. They are seeking a hands-on ML/LLM Engineer to build and optimize AI-native systems that blend structured and ...

Digital Links Inc

Java AI/LLM

Glen Lyn, VA · Remote

$52.25 - $67.50/hr

AI/LLM skill with * AI/LLM - hugging face model, OLAMA, LLAMA, Mistral * Agentic AI, Open AI, Gemini * Fine tuning of LLM * Lang chain, Lang flow, FAISS, vector database, Cosine similarity search.

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How much do llm ai jobs pay per year?

As of Aug 14, 2026, the average yearly pay for llm ai in the United States is $63,311.00, according to ZipRecruiter salary data. Most workers in this role earn between $51,000.00 and $69,500.00 per year, depending on experience, location, and employer.

What does an LLM AI do?

A typical day for someone in an LLM AI position involves designing, training, and evaluating large language models, collaborating with data scientists and software engineers, and reviewing system outputs for quality and accuracy. Professionals often attend team meetings to discuss project milestones, research new advancements, and troubleshoot model performance issues. The role tends to be highly collaborative, requiring frequent knowledge-sharing and coordination with other AI specialists, product managers, and, in some cases, external stakeholders. Staying up-to-date with the latest advancements in natural language processing and AI is an essential part of the job, ensuring that models remain state-of-the-art and impactful.

What are the key skills and qualifications needed to thrive in the LLM AI position?

To excel in an LLM AI (Large Language Model AI) role, candidates generally need a strong background in computer science, machine learning, and natural language processing, often supported by advanced degrees and experience with AI research or engineering. Familiarity with tools like Python, TensorFlow, PyTorch, and cloud computing platforms, as well as certifications in AI or data science, is highly valued. Excellent problem-solving, collaboration, and communication skills set standout professionals apart, enabling them to work effectively on cross-disciplinary teams. These skills and qualifications are crucial for developing, fine-tuning, and deploying cutting-edge language models that drive real-world AI applications.

What jobs can you do with an Llm Ai?

An LLM AI can be used in roles such as AI research scientist, machine learning engineer, data scientist, natural language processing specialist, or AI product developer. These jobs typically require skills in programming, data analysis, and understanding of AI models, often involving tools like Python and TensorFlow. Such positions are found in technology companies, research institutions, and startups focused on AI development.

What is an LLM AI?

An LLM AI job involves working with large language models (LLMs) to develop, train, optimize, and integrate AI-powered applications. Responsibilities may include natural language processing (NLP), prompt engineering, fine-tuning models, and ensuring ethical AI usage. These roles are common in AI research, software development, and data science fields.

Which Llm Ai is most in demand?

The most in-demand LLM AI roles typically involve expertise in natural language processing, machine learning, and deep learning frameworks such as TensorFlow or PyTorch. Skills in model fine-tuning, data management, and familiarity with popular models like GPT, BERT, or RoBERTa are highly sought after by employers across industries including tech, healthcare, and finance.
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Llm Ai jobs near you
Infographic showing various Llm Ai job openings in the United States as of August 2026, with employment types broken down into 76% Full Time, 20% Part Time, and 4% Contract. Highlights an 67% Physical, 4% Hybrid, and 29% Remote job distribution, with an average salary of $63,311 per year, or $30.4 per hour.

Rengo AI - AI Engineer

De Circle

New York, NY • On-site, Remote

$125K - $150K/yr

Full-time

Re-posted 25 days ago

Job description

Rengo AI is building the intelligence layer for fund management - starting with next-generation portfolio monitoring systems for investment teams.
Today, portfolio monitoring is fragmented across dashboards, spreadsheets, internal tools, and manual analyst workflows. Rengo replaces this with an AI-native monitoring layer that continuously interprets portfolio activity, risk, exposure, and performance across assets and strategies.
The Role
As a Founding AI Engineer, you will build the core system that powers AI-driven portfolio monitoring for institutional investors.
You will design systems that continuously:
  • ingest portfolio + market + position-level data
  • detect meaningful changes and anomalies
  • generate structured investment insights
  • explain performance and risk drivers in natural language + structured outputs

This is a high-reliability AI system, not a chatbot.
What You'll Build
1. AI Portfolio Monitoring Engine
  • Real-time and batch systems that monitor:
    • portfolio performance (PnL, attribution, drawdowns)
    • exposure shifts (sector, geography, asset class)
    • risk signals (volatility, correlation, concentration)
    • position-level changes
  • AI layer that converts raw portfolio data into:
    • alerts
    • summaries
    • explanations
    • actionable insights

2. Change Detection & Intelligence Layer
  • Build systems that detect:
    • significant portfolio movements
    • abnormal price/volume behavior in holdings
    • drift from target allocations
    • risk regime changes
  • Prioritization layer: what matters vs noise

3. AI-Generated Portfolio Narratives
  • Generate structured outputs such as:
    • daily / weekly portfolio reports
    • performance explanations ("why did we lose/gain?")
    • exposure breakdowns
    • risk commentary
  • Ensure outputs are:
    • auditable
    • grounded in data
    • consistent across runs

4. Data + Retrieval Systems for Funds
  • Integrate:
    • positions & holdings data
    • market data feeds
    • internal fund metadata
    • external news & filings (optional enrichment layer)
  • Build RAG pipelines over portfolio + market context

5. LLM Systems for Financial Reliability
  • Design LLM pipelines that:
    • avoid hallucinated financial reasoning
    • produce structured, verifiable outputs
    • ground insights in actual portfolio data
  • Build evaluation frameworks for correctness of financial narratives

Strong engineering background
  • 3-7+ years in backend, data engineering, or ML systems
  • Strong Python (mandatory)
  • Experience building production data systems or analytics platforms
LLM / AI systems experience
  • Experience building LLM applications in production
  • Strong understanding of:
    • RAG systems
    • structured generation (schemas, JSON outputs)
    • tool use / function calling
    • agent workflows
  • Awareness of failure modes in LLM reasoning (critical in finance)
Data-heavy systems mindset
  • Experience with:
    • time-series data
    • event-driven pipelines
    • analytics / observability systems
  • Comfort working with imperfect, high-volume financial data
Nice to Have
  • Experience in:
    • asset management / hedge funds / fintech
    • portfolio analytics or risk systems
    • trading / market data infrastructure
  • Familiarity with:
    • exposure/risk models
    • PnL attribution systems
    • BI / analytics platforms for finance
  • Experience with vector databases or hybrid retrieval systems

What Makes This Role Unique
  • You are building the core monitoring brain of a fund
  • Not dashboards - interpretation + intelligence
  • Systems you build directly influence investment decisions and risk awareness
  • High emphasis on:
    • correctness
    • traceability
    • reliability under uncertainty
  • You own the full stack: data → intelligence → insight delivery

Tech Direction
  • Python (core systems + AI orchestration)
  • LLM APIs (OpenAI / Anthropic / open-source models)
  • Postgres + time-series storage
  • Vector DB for semantic retrieval
  • Stream/batch processing pipelines
  • Cloud infrastructure (AWS/GCP)

Why Join
  • Define how AI monitors institutional portfolios
  • Replace manual analyst workflows with automated intelligence systems
  • Work on one of the hardest AI problems in finance: turning data into trustworthy interpretation
  • High ownership, early-stage, no legacy constraints

Apply