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Senior Engineer-AI & Full Stack Engineering Lead
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Drive code quality, testing, and engineering best practices across teams
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Added 26 mins ago
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As a Senior Software Engineer, you'll help advance a mission-critical fintech platform while partnering closely with Product, QA, DevOps, and Engineering teams to deliver scalable,…
Added 28 mins ago
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Added 30 mins ago
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Added 31 mins ago
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Added 33 mins ago
Senior Full Stack Software Engineer
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Added 36 mins ago
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Senior Software Engineer, Customer Financial Execution
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Added 43 mins ago
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Added 1 hour ago
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Added 1 hour ago
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Select Minds LLC

Senior Agentic AI Engineer

Dallas, TX, United States

$120,000 to $140,000 USD / year

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Job details

Pay: $120,000 to $140,000 USD / year
Experience: Experience: 8+ years
Posting language: English
Posted: Nov 5, 2026

Original job description

Benefits:

hybrid
Competitive salary
Opportunity for advancement

Job Title: Senior / Lead Agentic AI Engineer – LLM Evaluation & Observability Location: Dallas, TX (Hybrid) Duration: Long‑Term Experience: 8+ years Interview: Client round, in‑person

Important: This is a hands‑on engineering role. Candidates with practical experience building and evaluating AI applications are preferred over those whose experience is limited to AI strategy, architecture documentation, conventional QA automation, or using AI coding assistants.

We seek a Senior/Lead AI Engineer with experience building, orchestrating, monitoring, and evaluating production‑grade Generative AI and Agentic AI applications. The ideal candidate is expert in Amazon Bedrock, AgentCore, LangGraph, LangChain, RAG, LLM observability, and evaluation frameworks. The role focuses on creating reliable, scalable, production‑ready AI applications, emphasizing agent orchestration, AI testing, LLM evaluation, and continuous quality monitoring.

The candidate will collaborate with Engineering, QA, and platform teams to establish best practices for building, testing, monitoring, and improving AI‑powered applications. The testing focus is on GenAI evaluation, RAG pipeline assessment, agent behavior validation, LLM quality benchmarking, and automated evaluation frameworks, not conventional UI test automation.

Key Responsibilities

Agentic AI & GenAI Engineering

Design, develop, and deploy production‑grade agentic and generative AI applications.
Build and orchestrate AI agents using LangGraph, LangChain, Amazon Bedrock, and AgentCore.
Implement multi‑agent workflows, tool calling, state management, memory, and orchestration.
Create scalable LLM‑powered applications and reusable AI components.
Deploy agentic workflows with Amazon Bedrock AgentCore, handling runtime management, tool integration, and execution orchestration.

RAG & LLM Application Development

Design and implement Retrieval‑Augmented Generation (RAG) solutions.
Develop pipelines for document ingestion, chunking, embedding, retrieval, reranking, and context management.
Optimize retrieval accuracy, response quality, latency, and cost.
Apply grounding, hallucination reduction, and context‑relevance techniques.

LLM Evaluation & AI Testing

Build automated evaluation frameworks with RAGAS, DeepEval, and similar tools.
Evaluate RAG pipelines using metrics such as Faithfulness, Answer Relevancy, Context Precision, Context Recall, and Answer Correctness.
Implement LLM‑as‑a‑Judge techniques to assess generated responses, retrieval quality, agent behavior, and workflow execution.
Establish evaluation datasets, benchmarks, and regression‑testing pipelines for continuous quality assessment.

3a. Agent Evaluation and Observability

Implement end‑to‑end tracing of multi‑agent workflows, including tool invocation, agent handoffs, execution paths, and failure handling.
Establish observability practices to detect hallucinations, reasoning failures, tool‑calling errors, and execution inconsistencies.

AI Observability & Monitoring

Implement tracing, monitoring, and observability with OpenTelemetry, LangSmith, and Langfuse.
Track LLM requests, agent execution flows, tool calls, latency, token consumption, errors, and performance.
Develop debugging mechanisms for complex multi‑step agentic workflows.
Create monitoring practices to detect performance degradation and operational issues.

AWS AI Platform & Production Engineering

Deploy AI applications using Amazon Bedrock and Bedrock AgentCore.
Integrate foundation models, agent frameworks, APIs, and enterprise data sources.
Apply production‑readiness practices covering scalability, reliability, security, and performance.
Support CI/CD integration and automated evaluation of AI applications.
Optimize model selection, inference costs, latency, and resource utilization.

Technical Leadership & Collaboration

Provide technical guidance to engineering and QA teams.
Establish reusable frameworks, implementation patterns, and best practices.
Collaborate with architecture, DevOps, QA, and engineering teams.
Participate in design discussions, code reviews, proof‑of‑concept development, and implementation.
Mentor engineers and help teams adopt effective AI development and testing practices.

Required Technical Skills

Agentic AI & Orchestration

LangGraph
LangChain
Amazon Bedrock
Amazon Bedrock AgentCore
AI agents, multi‑agent orchestration, tool calling, workflow management

Generative AI & RAG

Large Language Models (LLMs)
Retrieval‑Augmented Generation (RAG)
Embeddings and vector search
Prompt engineering and context management
LLM application development

LLM Evaluation & Testing

DeepEval
LLM‑as‑a‑Judge techniques
Evaluation metrics and benchmarking
Automated evaluation and regression testing
Assessment of hallucination, faithfulness, relevance, and response quality
Hands‑on experience with RAGAS, DeepEval, or equivalent frameworks
Strong understanding of RAG evaluation metrics (Faithfulness, Answer Relevancy, Context Precision, Context Recall, Answer Correctness)
Experience building automated evaluation pipelines for RAG‑based applications

AI Observability & Monitoring

OpenTelemetry
LangSmith
Langfuse
LLM tracing, debugging, and performance monitoring

Production Engineering

Deploying and operating production‑grade GenAI applications
API integration and backend engineering
CI/CD and automated testing integration
Scalability, reliability, security, and performance optimization
Strong hands‑on programming experience in Python.
Experience developing REST APIs and integrating LLM frameworks with enterprise applications.
Familiarity with asynchronous programming, API integrations, and backend development.

Preferred Qualifications

8+ years of software engineering or IT experience.
Demonstrated hands‑on experience building and deploying GenAI and Agentic AI applications.
Recent experience in LLM engineering, agent orchestration, and AI evaluation.
Experience with enterprise‑scale AI implementations.
Familiarity with AWS cloud services and enterprise AI security practices.
Experience establishing AI quality standards and evaluation methodologies.
Strong problem‑solving, communication, and technical collaboration skills.

What Success Looks Like

Development and deployment of production‑grade agentic AI applications.
Reliable orchestration and execution of AI agents and workflows.
Effective LLM evaluation and automated AI testing frameworks.
Improved AI application accuracy, reliability, observability, and performance.
Reusable engineering practices for building and maintaining enterprise GenAI solutions.

Flexible work from home options available.

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More about this job

Skills

LangChain
Workflow
Debugging
AWS
CI/CD
Test automation
Problem solving
Communication skills
Collaboration

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