Why This Role Stands Out
As an AI Engineer at Promantus, you'll leverage your expertise in GenAI and Python to drive innovative solutions, with ample opportunity for professional growth in a cutting-edge technology environment. This role is perfect for experienced engineers who thrive on creative problem-solving and independent debugging, so don't hesitate to apply and shape the future of AI with us!
Quick Overview
Job Description
Role: Senior Generative AI Engineer (7-10+ years’ experience)
Location: Plano, TX (onsite client interview and worksite)
Duration: 12+ Months
The below skills are mandatory. The candidate should be able to commit to an in-person interview in Plano; hence, local candidates will be preferred.
Required Qualifications
Core Software Engineering
- 8+ years of experience in software engineering with strong expertise in Python.
- Hands-on experience designing and developing microservices-based architectures using FastAPI or similar Python frameworks.
- Experience building scalable, secure, and production-grade APIs.
Cloud & Data Platforms
- Strong experience with Microsoft Azure, including AI, data, and application services.
- Expertise in MongoDB and NoSQL database design, optimization, and data modeling.
- Experience deploying and managing cloud-native applications and containerized workloads.
Generative AI & Agentic Systems
- Proven experience developing Generative AI solutions using modern LLM frameworks.
- Hands-on experience building AI Agents, multi-agent systems, and autonomous workflows.
- Strong understanding of Retrieval-Augmented Generation (RAG) architectures, including:
- Knowledge RAG
- Graph RAG
- Hybrid retrieval systems
- Context-aware retrieval pipelines
- Experience designing enterprise knowledge bases, semantic search solutions, and context-building frameworks.
Large Language Models
- Strong understanding of Large Language Models (LLMs) and Small Language Models (SLMs).
- Experience with model selection, prompt engineering, grounding strategies, fine-tuning approaches, and inference optimization.
- Familiarity with model performance, latency, cost optimization, and governance considerations.
AI Evaluation & Observability
- Hands-on experience implementing Agentic Evaluation frameworks, including tools such as DeepEval.
- Experience designing evaluation metrics for:
- Retrieval quality
- Hallucination detection
- Answer relevance
- Agent task completion
- Safety and reliability
- Experience with AI observability and monitoring platforms, particularly Arize AI, for production monitoring, tracing, and model performance analysis.
Preferred Qualifications
- Experience with vector databases and embedding models.
- Familiarity with LangChain, LangGraph, Semantic Kernel, LlamaIndex, or similar AI orchestration frameworks.
- Experience building enterprise AI applications with governance, security, and compliance considerations.
- Knowledge of MLOps/LLMOps practices, CI/CD pipelines, and model lifecycle management.
- Experience working in Agile product development environments.
Key Responsibilities
- Design and develop scalable AI-powered microservices and APIs using Python and FastAPI.
- Build and maintain enterprise-grade RAG and Graph RAG solutions.
- Develop intelligent agentic workflows that leverage organizational knowledge and business context.
- Optimize knowledge retrieval, context orchestration, and response quality for AI applications.
- Implement evaluation frameworks and monitoring solutions using DeepEval and Arize.
- Deploy, monitor, and maintain AI applications on Azure.
- Collaborate with product, engineering, and business teams to translate use cases into production-ready AI solutions.
Must have existing hhands-onexperience
- Azure AI Foundry
- Azure OpenAI Service
- Semantic Kernel
- LangGraph
- Neo4j or Graph Databases
- Vector Databases (Azure AI Search, Pinecone, Weaviate, Qdrant)
- Kubernetes and Docker
- GitHub Actions / Azure DevOps
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