AIML engineer (Python)

Frisco, TX - USA

Monthly Salary: Not Disclosed

Posted on: 5 hours ago

Vacancies: 1 Vacancy

Job Summary

Role: AI/ML engineer (Python)

Location: Frisco Texas

Key Responsibilities

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 GitHub.
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:

BS/Advanced degree in quantitative fields: Computer Science Data Science Engineering Business Analytics Math/Statistics or a related field
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. LangGraph AutoGen CrewAI or LangChain 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

Role: AI/ML engineer (Python) Location: Frisco Texas Key Responsibilities 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 ha...

Role: AI/ML engineer (Python)

Location: Frisco Texas

Key Responsibilities

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 GitHub.
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:

BS/Advanced degree in quantitative fields: Computer Science Data Science Engineering Business Analytics Math/Statistics or a related field
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. LangGraph AutoGen CrewAI or LangChain 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