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AI Engineer — Agentic Systems

Saicon Consultants Inc.

Austin, Texas • Today

Easy Apply

Third Party, Contract

Depends on Experience

AI Engineer Agentic Systems

Cynet Systems

Austin, Texas • Today

Easy Apply

Third Party, Contract

Depends on Experience

Senior Full-Stack AI Engineer (Agentic Systems)

Quzara LLC

Remote • Today

Full-time

AI Architect - Senior AI Engineer - Agentic Systems (Remote)

NTT DATA, Inc.

Remote or Plano, Texas • Today

Full-time

USD 122,648.00 - 283,906.00 per year

Core Applied AI Engineer Agentic AI & Security

BirdsVue LLC

Austin, Texas • 7d ago

Easy Apply

Contract

60 - 65

Agentic AI Systems Architecture Fellow

AMD (Advanced Micro Devices)

Austin, Texas • Today

Full-time

USD 204,400.00 per year

Staff Engineer I – Agentic AI Platform Deployment Engineer

Feuji Inc

Remote • 5d ago

Easy Apply

Contract

Depends on Experience

Senior AI Systems Performance Engineer

AMD (Advanced Micro Devices)

Austin, Texas • Today

Full-time

USD 156,100.00 per year

Lead Systems Engineer, Enterprise AI Server Platform Validation

AMD (Advanced Micro Devices)

Austin, Texas • Today

Full-time

USD 142,800.00 per year

Staff Systems Engineer, Collaboration Platforms & AI

Visa Inc.

Austin, Texas • Today

Full-time

USD 133,300.00 - 251,100.00 per year

Staff Systems Engineer, Collaboration Platforms & AI

Visa

Austin, Texas • 3d ago

Full-time

USD 133,300.00 - 251,100.00 per year

AI Systems Performance Engineer

AMD (Advanced Micro Devices)

Austin, Texas • Today

Full-time

USD 106,470.00 per year

Senior Engineer – AI

Ekman Associates, Inc.

Remote • 5d ago

Easy Apply

Full-time

Depends on Experience

Principal Systems Development Engineer - Enterprise Architecture for AI-enabled engineering applications and platforms

Dell Technologies

Round Rock, Texas • Today

Full-time

USD 170,000.00 - 200,000.00 per year

Senior AI Engineer (Agentic AI, AWS Bedrock & Python)

Simple Solutions

Remote • Today

Easy Apply

Contract, Third Party

70 - 95

Senior AWS Application Architect (AI & Agentic Systems)

INSPYR Solutions

Remote • 15d ago

Easy Apply

Contract

$75 - $85

Agentic AI Engineer

Shiv Software Experts LLC

Remote or Hybrid in Oakland, California • 22d ago

Easy Apply

Contract

Depends on Experience

Full Stack Engineer, AI Systems, Artificial Intelligence (AI) Required, Work From Home - G

Next Step Systems

Remote • Today

Easy Apply

Full-time

$140,000 - $190,000

Senior AI Engineer: Generative AI, Agentic AI & RAG

Nimara Ventures Pvt Ltd

Remote • 2d ago

Easy Apply

Contract

45 - 55

GenAI and Agentic AI Engineer

Kforce Technology Staffing

Remote or Tampa, Florida • Today

Contract

$75 - $85 hourly

AI Platform Engineer -Agentic AI

TekAssembly

Remote • 20d ago

Easy Apply

Contract

Depends on Experience

Lead AI Engineer with agentic coding harnesses

EPAM Systems

Remote • Today

Full-time

Only W2: AI Prompt Engineer / Agentic AI Engineer, 100% Remote

Tror

Remote • 8d ago

Easy Apply

Third Party, Contract

Depends on Experience

Senior Agentic AI Engineer (Palantir Foundry and AIP)

Excellent Pro Group Inc.

Remote • 15d ago

Easy Apply

Contract

Depends on Experience

Network Engineer – Automation & AI

DeepData Inc.

Remote • 13d ago

Easy Apply

Full-time

Depends on Experience

Senior AI Engineer with RAG and Agentic architectures

EPAM Systems

Remote • Today

Full-time

Agentic AI Engineer - Madison, WI, Columbus, OH, Chicago, IL, Minneapolis, MN, Detroit, MI.

TechniPros, LLC

Remote or Madison, Wisconsin • Yesterday

Easy Apply

Contract

Depends on Experience

Agentic AI Platform Engineer

Tech Rakers

Remote • Today

Easy Apply

Contract

Depends on Experience

Senior Front-End Agentic AI Engineer

LTS

Remote • 28d ago

Easy Apply

Full-time

167000 - 204000

AGENTIC AI ENGINEER - AI/LLM

3AMIGOSIT LLC

Remote • Today

Easy Apply

Contract

Job Details

Skills

Summary

Role Summary
We are hiring an AI Engineer to design, build, and operate agentic AI systems — LLM-powered agents that reason over enterprise data, call tools, and execute multi-step workflows end to end. This is a hands-on engineering role at the center of how we automate complex technical and operational work. You will take ambiguous problems, decompose them into agent workflows, and ship production services that engineering teams depend on daily.
This is an ideal role for an engineer with a strong Python and ML foundation who has already shipped LLM or RAG applications and now wants to go deeper into orchestration, tool-calling, and autonomous multi-agent architectures. You will work within an established platform and architecture, with senior engineers available for design review — but you will own your components from prototype through production.

Key Responsibilities
Agent Design & Orchestration

Build and ship LLM-powered agents that plan, call tools, and complete multi-step tasks using frameworks such as LangChain, LangGraph, or equivalent orchestration libraries.
Implement multi-agent workflows — planner, executor, reviewer, and specialist roles — including state handling, hand-off logic, retries, and graceful failure paths.
Design and expose tools to agents via function-calling and Model Context Protocol (MCP) servers, wrapping internal APIs, databases, and enterprise systems as safe, well-scoped agent capabilities.

Retrieval & Knowledge Systems

Build and operationalize Retrieval-Augmented Generation (RAG) pipelines: chunking, embedding, vector and hybrid search, re-ranking, and context assembly.
Integrate structured, semi-structured, and unstructured sources — SharePoint, Confluence, Jira, REST APIs, Excel, and SQL warehouses — into retrieval and agent context layers.
Continuously improve answer grounding and reduce hallucination through better retrieval, prompt design, and context management.

Engineering & Deployment

Develop production Python services and APIs for agent and model inference using FastAPI, containerized with Docker and deployed through standard CI/CD pipelines.
Instrument agents with tracing, token and cost accounting, latency monitoring, and structured logging so that behavior is observable and debuggable in production.
Apply MLOps and LLMOps practices including version control for prompts and configs, automated regression testing, and safe rollout of model or prompt changes.

Evaluation & Quality

Build evaluation harnesses for agent workflows — golden datasets, task-level success metrics, LLM-as-judge scoring, and regression suites that gate releases.
Measure and tune for accuracy, latency, and cost, and make explicit trade-offs between them based on the use case.

Collaboration

Partner with domain experts, data engineers, and product stakeholders to turn manual workflows into reliable automated agents.
Deliver results through interfaces users actually adopt — Streamlit apps, chat interfaces, dashboards, and embedded integrations.
Write clear design notes and runbooks, and participate actively in code and design reviews.

Required Qualifications

3–5 years of professional software, ML, or data engineering experience, including at least 1 year building LLM-based or GenAI applications.
Strong Python engineering skills — not just notebooks. Production code, testing, packaging, async patterns, and API development with FastAPI or similar.
Hands-on experience with at least one LLM orchestration framework (LangChain, LangGraph, LlamaIndex, Semantic Kernel, or an equivalent) in a shipped application.
Demonstrated experience building RAG systems: embeddings, vector stores (e.g., FAISS, pgvector, Pinecone, Chroma), and retrieval quality tuning.
Practical command of prompt engineering, structured output, function/tool calling, and context-window management.
Working knowledge of SQL and cloud data platforms (Snowflake, Databricks, or equivalent), and comfort writing queries against enterprise schemas.
Experience containerizing and deploying services with Docker and shipping through CI/CD on a major cloud (Azure, AWS, or Google Cloud Platform).
Solid grounding in ML fundamentals and familiarity with PyTorch, TensorFlow, or HuggingFace Transformers.
Clear written and verbal communication, and the judgment to escalate when a problem is under-specified rather than guessing.

Preferred Qualifications

Experience building or consuming Model Context Protocol (MCP) servers, or comparable agent tool-integration layers.
Production experience with multi-agent or agentic workflow systems, including human-in-the-loop checkpoints and approval gates.
Familiarity with LLM observability and evaluation tooling such as LangSmith, Langfuse, Weights & Biases, or MLflow.
Experience with workflow orchestration tools — Airflow, Dagster, Prefect — or analytics engineering with DBT.
Exposure to model fine-tuning or parameter-efficient adaptation (LoRA/PEFT) and knowing when it is — and is not — the right answer.
Background in a technical engineering domain (semiconductor, hardware validation, manufacturing, or similar) where agents operate over specialized data.
Experience with AI governance, responsible-AI review, or enterprise security requirements for LLM deployments.

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: saicon
Position Id: 26-90339
Posted 12 hours ago

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Job Title: Agentic AI Developer/ Architect Location: local to Austin and available to work in a hybrid model, with 3 days per week onsite Job Type: Long-term Contract Job Description We are seeking an experienced AI Architect with expertise in Generative AI, Agentic AI, and Java development to design and implement scalable, secure, AI-driven enterprise applications. The ideal candidate will have strong experience building Java-based microservices while integrating LLMs, RAG pipelines, vector da

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Job Details

Skills

Summary

Build and ship LLM-powered agents that plan, call tools, and complete multi-step tasks using frameworks such as LangChain, LangGraph, or equivalent orchestration libraries.
Implement multi-agent workflows — planner, executor, reviewer, and specialist roles — including state handling, hand-off logic, retries, and graceful failure paths.
Design and expose tools to agents via function-calling and Model Context Protocol (MCP) servers, wrapping internal APIs, databases, and enterprise systems as safe, well-scoped agent capabilities.

Retrieval & Knowledge Systems

Build and operationalize Retrieval-Augmented Generation (RAG) pipelines: chunking, embedding, vector and hybrid search, re-ranking, and context assembly.
Integrate structured, semi-structured, and unstructured sources — SharePoint, Confluence, Jira, REST APIs, Excel, and SQL warehouses — into retrieval and agent context layers.
Continuously improve answer grounding and reduce hallucination through better retrieval, prompt design, and context management.

Engineering & Deployment

Develop production Python services and APIs for agent and model inference using FastAPI, containerized with Docker and deployed through standard CI/CD pipelines.
Instrument agents with tracing, token and cost accounting, latency monitoring, and structured logging so that behavior is observable and debuggable in production.
Apply MLOps and LLMOps practices including version control for prompts and configs, automated regression testing, and safe rollout of model or prompt changes.

Evaluation & Quality

Build evaluation harnesses for agent workflows — golden datasets, task-level success metrics, LLM-as-judge scoring, and regression suites that gate releases.
Measure and tune for accuracy, latency, and cost, and make explicit trade-offs between them based on the use case.

Collaboration

Partner with domain experts, data engineers, and product stakeholders to turn manual workflows into reliable automated agents.
Deliver results through interfaces users actually adopt — Streamlit apps, chat interfaces, dashboards, and embedded integrations.
Write clear design notes and runbooks, and participate actively in code and design reviews.

Required Qualifications

3–5 years of professional software, ML, or data engineering experience, including at least 1 year building LLM-based or GenAI applications.
Strong Python engineering skills — not just notebooks. Production code, testing, packaging, async patterns, and API development with FastAPI or similar.
Hands-on experience with at least one LLM orchestration framework (LangChain, LangGraph, LlamaIndex, Semantic Kernel, or an equivalent) in a shipped application.
Demonstrated experience building RAG systems: embeddings, vector stores (e.g., FAISS, pgvector, Pinecone, Chroma), and retrieval quality tuning.
Practical command of prompt engineering, structured output, function/tool calling, and context-window management.
Working knowledge of SQL and cloud data platforms (Snowflake, Databricks, or equivalent), and comfort writing queries against enterprise schemas.
Experience containerizing and deploying services with Docker and shipping through CI/CD on a major cloud (Azure, AWS, or Google Cloud Platform).
Solid grounding in ML fundamentals and familiarity with PyTorch, TensorFlow, or HuggingFace Transformers.
Clear written and verbal communication, and the judgment to escalate when a problem is under-specified rather than guessing.

Preferred Qualifications

Experience building or consuming Model Context Protocol (MCP) servers, or comparable agent tool-integration layers.
Production experience with multi-agent or agentic workflow systems, including human-in-the-loop checkpoints and approval gates.
Familiarity with LLM observability and evaluation tooling such as LangSmith, Langfuse, Weights & Biases, or MLflow.
Experience with workflow orchestration tools — Airflow, Dagster, Prefect — or analytics engineering with DBT.
Exposure to model fine-tuning or parameter-efficient adaptation (LoRA/PEFT) and knowing when it is — and is not — the right answer.
Background in a technical engineering domain (semiconductor, hardware validation, manufacturing, or similar) where agents operate over specialized data.
Experience with AI governance, responsible-AI review, or enterprise security requirements for LLM deployments.

Dice Id: saicon
Position Id: 26-90339
Posted 12 hours ago

Fitment

Dice Job Match Score™

To see how well you match this job, please log in or create an account.

Once logged in, be sure to complete your profile to get the most accurate match score.

Create job alert

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