Role: AI/ML Engineer

Location: Plano, TX

Duration: Fulltime

Job Description:

We are seeking an AI ML Engineer to design, develop, and deploy Generative AI applications, with a focus on Retrieval-Augmented Generation (RAG) and agentic workflows. The ideal candidate will have hands-on experience building applications using LLMs, vector databases, and agent harnesses, along with experience in Azure stack. This role involves developing scalable, production-grade AI solutions that integrate enterprise data with advanced reasoning and automation capabilities

Responsibilities:

Design and build agentic applications using Langchain or similar, integrating enterprise data sources (Azure Databricks, documents, APIs)
Build and optimize RAG vector databases for use by agents using tools like Azure AI Search, or similar
Integrate LLMs (Azure OpenAI, GPT models) into enterprise applications with proper prompt engineering and optimization
Implement evaluation, monitoring, and guardrails for AI systems (accuracy, bias, hallucination control)
Collaborate with data engineers and application teams to integrate AI solutions into production systems
Optimize application performance, latency, and cost for large-scale deployments
Communicate insights clearly to both technical and non-technical stakeholders

Minimum Qualifications

2+ years of experience in AI/ML Engineering, Data Science, or related field
Experience using data from Azure Databricks or similar relational databases
Strong hands-on experience with Python (core development language), Git, Github; LLM frameworks especially Langchain; RAG architectures and vector search concepts
Experience with Azure AI ecosystem, including Azure OpenAI models and Azure AI Search
Understanding of AI ML concepts, LLMs, vector databases, RAG, agents
Familiarity with APIs, microservices, MCP and scalable system design
Strong communication skills

The expected salary range for this position is between $74,500 to $94,500 annually. The actual salary may vary based upon several factors including, but not limited to, relevant skills/experience, time in role, base salary of internal peers, prior performance, business line, and geographic/office location.

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AI/ML Engineer