Senior AI Engineer

Overview

Job Title: AI Engineer

Location: Dallas, TX / Atlanta, GA

Duration: 6 months

Job Description:

Experience Desired: 8 Years.

Key required skills

The candidate should be able to serve as the lead technical contributor for designing and deploying enterprise-grade AI systems. This role demands a senior AI engineer who can handle high-level architectural design and hands-on implementation of complex agentic workflows. The candidate will be responsible for building the “AIObserve” ecosystem, ensuring that probabilistic AI outputs are translated into deterministic, secure, and high-value business outcomes.

Core Responsibilities

• Architecting Agentic Systems: Design and implement multi-agent systems using the Model Context Protocol (MCP) to enable seamless tool-calling across platforms like Atlassian and Git Hub.
• Enterprise RAG Implementation
  :
  Lead the development of sophisticated Retrieval-Augmented Generation (RAG) layers, integrating vector databases like Milvus with enterprise knowledge bases (Jira/Confluence).
• Orchestration & Workflow Automation: Build and optimize backend services using FastAPI and Azure Bot Service to handle real-time message routing and automated ticket fulfillment.
• High-Privilege Automation: Develop secure browser automation scripts using Python and Playwright to handle complex tasks such as RBAC validation and post-true-up process automation.
• Security & RBAC Engineering: Engineer robust Role-Based Access Control (RBAC) within AI agents to ensure high-privilege operations are executed safely and within compliance.
• Performance Tuning: Optimize system latency to ensure AI responses and backend acknowledgments meet strict enterprise thresholds (< 7 seconds).
• Architecting Observability Pipelines:
  Design and implement end-to-end telemetry for AI agents. This includes capturing not just system logs, but also LLM-specific traces (latency, token usage, and “hallucination” scores) to provide a 360-degree view of system health
• LLMOps Infrastructure
  :
  Own the deployment lifecycle, including CI/CD for prompt engineering, automated testing of RAG retrieval accuracy, and monitoring for “model drift” in production.
• Cross-functional Collaboration
  :
  Working with product managers, data scientists, and business stakeholders to translate needs into AI solutions.

Preferred Qualifications:

• 7 years of experience in applied AI engineering or related role with 2 years in agentic development, and/or with a combination of context/prompt engineering
• Expert-level Python proficiency with emphasis on modular, object-oriented code, strict typing, and rigorous unit/integration testing for production
• Experience with building both conversational agents and workflow agentic processes in production
• Applied experience with multiple LLM stacks/frameworks (e.g., OpenAI, Claude, Gemini, RAG pipelines), and agent orchestration systems (e.g., Lang Graph, Auto Gen, CrewAI, or Lang Chain building collaborative autonomous and complex AI workflows
• Demonstrated comfort with prompt design strategies (chain-of-thought, few-shot) and context window optimization to ensure high-quality LLM outputs
• Familiarity with cloud platforms (AWS/Azure), REST APIs, and containerization (Docker, K8s)
• Experience implementing and managing Vector Databases (e.g., Pinecone, Milvus, Weaviate) for RAG (Retrieval-Augmented Generation) pipelines.
• Experience with Azure bot services, Fast API, OAuth for API security is recommended.
• Proficiency in Databricks and SQL (DDL/DML) driving scalable data architecture and holistically integrating prompt designs, vector databases, and memory strategies to deliver advanced LLM solutions
• Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost
• Passion for staying abreast of the latest AI research and AI systems, and judiciously applying novel techniques in production
• Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers

Key

Skills:

AI Engineering, Prompt Engineering, Python, RAG