6 days ago
Responsibilities
- Engage directly with customers and business stakeholders to translate complex use cases into AI and generative AI solutions.
- Design, develop, deploy, and operationalize production applications using LLMs and machine learning technologies.
- Build RAG pipelines using enterprise data, embeddings, vector databases, and semantic search.
- Develop AI agents and agentic workflows involving tool calling, orchestration, memory, and multi-step reasoning.
- Integrate AI capabilities with enterprise applications, APIs, databases, cloud platforms, and business workflows.
- Develop backend services and APIs using Python, FastAPI, or similar frameworks.
- Implement prompt engineering, model evaluation, guardrails, responsible AI, security, privacy, access control, and data governance practices.
- Deploy scalable AI workloads using cloud platforms, Docker, and Kubernetes, with appropriate LLMOps/MLOps monitoring and observability.
- Create proof-of-concepts and convert successful prototypes into production-ready solutions.
- Troubleshoot models, prompts, retrieval pipelines, APIs, infrastructure, and customer-environment issues.
- Collaborate with engineering and product teams to incorporate reusable customer solutions into the broader AI platform.
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 and production application development using generative AI and LLMs.
- Strong understanding of RAG, embeddings, vector databases, semantic search, prompt engineering, and agentic AI.
- Experience integrating AI applications with APIs, enterprise databases, and cloud services.
- Hands-on experience with at least one major cloud platform: Microsoft Azure, AWS, or GCP.
- 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 through production deployment.
- Excellent customer-facing communication, stakeholder-management, troubleshooting, and problem-solving abilities.
- Ability to work effectively in fast-paced environments with ambiguous or evolving customer requirements.
- Preferred experience includes Azure OpenAI Service, Azure AI Foundry, Azure AI Search, Amazon Bedrock, Google Vertex AI, AI agents, copilots, conversational AI, autonomous workflows, LLM evaluation, observability, tracing, hallucination detection, AI guardrails, PyTorch, and TensorFlow.
Tech Stack
Categories
Forward Deployed
About Brillio
Brillio is a privately held digital engineering and enterprise AI services firm that helps Fortune 1000 companies modernize products, data, and infrastructure. It combines consulting and managed services with an accelerator platform (ADAM) across cloud, AI, data engineering, customer experience, and infrastructure workstreams. Founded in 2014 and headquartered in Dallas, it operates delivery centers across North America, Europe, and Asia, with focus areas that include healthcare and life sciences.