Agentic AI Engineer

Compunnel, Inc.; Dallas (TX); On-site; USD 120,000 - 150,000; Full time

Job summary

A leading tech solutions company is looking for an experienced Agentic AI Engineer to design and implement AI solutions that enhance production reliability and reduce risk. The candidate should have over 5 years of software development experience, particularly in Python, and a strong understanding of ML systems and LLM applications. This position will involve collaboration with production teams to optimize AI implementations.

Qualifications

• 5+ years of software development experience in Python, C/C++, Go, or Java.
• 3+ years of experience designing and launching production ML systems.
• Hands-on experience with LLMs and building RAG-based applications.

Responsibilities

• Design and implement tool-calling agents for agentic AI.
• Build evaluation frameworks for open-source LLMs.
• Integrate agents into production operations and support.

Skills

Software development experience

Designing production ML systems

API integration

Prompt engineering

Problem-solving

Communication

Tools

AWS

Terraform

Python

C/C++

Go

Java

Job description

We are seeking an experienced Agentic AI Engineer to design and implement GenAI agentic solutions that enhance reliability, reduce risk, and optimize cost in large-scale production environments.

This role focuses on building intelligent agents capable of diagnosing, reasoning, and executing actions in runtime ecosystems to support production operations and improve productivity.

Key Responsibilities

• Agentic AI System Development: Design and implement tool-calling agents that integrate retrieval, structured reasoning, and secure action execution (e.g., function calling, policy enforcement) using MCP protocol.
• Engineer safety guardrails and enforce least-privilege access.
• LLM Productionization: Build evaluation frameworks for open-source and foundational LLMs.
• Implement retrieval pipelines, prompt synthesis, response validation, and self-correction loops tailored to production operations.
• Runtime Ecosystem Integration: Connect agents to observability, incident management, and deployment systems to enable automated diagnostics, runbook execution, remediation, and post-incident summarization with traceability.
• User Collaboration: Partner with production engineers and application teams to translate operational challenges into agentic AI roadmaps.
• Define objective functions linked to reliability, risk reduction, and cost efficiency.
• Safety, Reliability & Governance: Build validator models, adversarial prompts, and policy checks. Enforce deterministic fallbacks, circuit breakers, and rollback strategies.
• Instrument continuous evaluations for usefulness, correctness, and risk.
• Performance Optimization: Improve cost and latency through prompt engineering, context management, caching, model routing, and distillation.
• Use batching, streaming, and parallel tool-calls to meet service-level objectives under real-world load.
• RAG Pipeline Development: Curate domain knowledge, build data-quality validation frameworks, and establish feedback loops to maintain knowledge freshness.
• Engineering Excellence: Lead design reviews, promote rigorous experimentation, and mentor peers on agent architectures, evaluation methodologies, and safe deployment practices.

Required Qualifications

• 5+ years of software development experience in Python, C/C++, Go, or Java (Python preferred).
• 3+ years of experience designing and launching production ML systems, including model deployment, evaluation, monitoring, and fine-tuning workflows.
• Practical experience with LLMs: API integration, prompt engineering, fine-tuning, and building RAG-based applications with tool-using agents.
• Familiarity with commercial and open-source LLMs (e.g., OpenAI, Gemini, Llama, Qwen, Claude).
• Strong foundation in applied statistics, machine learning concepts, algorithms, and data structures.
• Excellent problem-solving and communication skills with a focus on measurable business impact.

Preferred Qualifications

• Experience with cloud infrastructure (preferably AWS), including ECS/EKS, Lambda, S3, DynamoDB, Redshift, Step Functions, SageMaker, and infrastructure-as-code tools like Terraform or CloudFormation.
• Participation in platform modernization and performance engineering initiatives.