Consultant - R01573317

Brillio•Saint Louis, MO

About The Position

We are looking for an experienced AI Engineer – Forward Deployed Engineer (FDE) to design, build, deploy, and operationalize AI solutions directly within customer environments. The role combines AI/ML and Generative AI engineering with customer engagement, solution architecture, and production deployment. The engineer will work with customers, product teams, data engineers, and platform teams to turn business requirements into scalable AI solutions and accelerate adoption from prototype through production.

Requirements

Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
Strong hands-on programming experience with Python.
Experience developing production applications using Generative AI and LLMs.
Strong understanding of RAG, embeddings, vector databases, semantic search, prompt engineering, and agentic AI.
Experience integrating AI applications with REST APIs, enterprise databases, and cloud services.
Hands-on experience with at least one major cloud platform: Microsoft Azure, AWS, or GCP.
Good understanding of software engineering principles, Git, CI/CD, testing, containerization, and API development.
Knowledge of machine learning concepts, model evaluation, inference, and performance optimization.
Experience taking AI solutions from prototype or proof-of-concept to production deployment.
Strong troubleshooting and problem-solving skills.
Excellent customer-facing communication and stakeholder-management abilities.
Ability to work in fast-paced environments with ambiguous or evolving customer requirements.

Nice To Haves

Experience with Azure OpenAI Service, Azure AI Foundry, Azure AI Search, Amazon Bedrock, Google Vertex AI, or similar AI platforms.
Experience building AI agents, copilots, conversational AI, or autonomous workflows.
Knowledge of LLM evaluation techniques, observability, tracing, hallucination detection, and AI guardrails.
Experience with PyTorch, Tensor

Responsibilities

Work directly with customers and business stakeholders to understand complex use cases and translate them into AI and Generative AI solutions.
Design, develop, and deploy production-grade applications using LLMs and machine learning technologies.
Build RAG pipelines using enterprise data sources, embeddings, vector databases, and semantic search.
Develop AI agents and agentic workflows, including tool calling, orchestration, memory, and multi-step reasoning.
Integrate AI capabilities with enterprise applications, APIs, databases, cloud platforms, and business workflows.
Perform prompt engineering and optimization, model evaluation, and guardrail implementation.
Develop backend services and APIs using Python, FastAPI, or similar frameworks.
Implement vector-search solutions using Azure AI Search, Pinecone, Weaviate, FAISS, or similar platforms.
Design and implement scalable AI workloads on Azure, AWS, or Google Cloud Platform.
Containerize and deploy AI applications using Docker and Kubernetes.
Establish LLMOps/MLOps practices for deployment, monitoring, evaluation, observability, versioning, and continuous improvement.
Develop proof-of-concepts and convert successful prototypes into production-ready solutions.
Troubleshoot AI application issues across models, prompts, retrieval pipelines, APIs, infrastructure, and customer environments.
Implement responsible AI practices covering security, privacy, access control, hallucination mitigation, content safety, and data governance.
Collaborate with engineering and product teams to incorporate reusable customer solutions and feedback into the broader AI platform.