Sr. AI Engineer with Databricks experience
TalentOla•Irving, TX
•Hybrid
About The Position
We are hiring a Senior AI Engineer with strong experience in customized LLMs, RAG pipelines, and Databricks Lakehouse. The role involves building and deploying enterprise-grade GenAI solutions using tools like Mosaic AI, MLflow, and vector databases, along with expertise in Python, cloud platforms, and AI orchestration frameworks.
Requirements
- 8+ Years of experience
- Engineering Degree – BE/ME/BTech/MTech/BSc/MSc.
- Strong hands-on experience with Databricks and Lakehouse architecture.
- Experience with Databricks Mosaic AI, MLflow, Delta Lake, and Unity Catalog.
- Strong programming skills in Python and SQL.
- Hands-on experience with LLMs such as GPT, Llama, Mistral, Claude, or similar models.
- Experience with RAG architectures, embeddings, and vector search implementations.
- Knowledge of LangChain, LangGraph, Semantic Kernel, or similar AI orchestration frameworks.
- Experience with cloud platforms such as AWS, Azure, or GCP.
- Familiarity with APIs, Docker, Kubernetes, and microservices architecture.
- Understanding of AI governance, model evaluation, and monitoring.
Nice To Haves
- Technical certification in multiple technologies is desirable.
- Experience deploying GenAI applications in enterprise environments.
- Knowledge of distributed computing and Spark optimization.
- Experience with Databricks Model Serving and AI Gateway.
- Familiarity with CI/CD and MLOps practices.
- Experience with multimodal AI models and AI agents.
Responsibilities
- Design, develop, and deploy customized LLM-based applications
- Build scalable Generative AI and RAG (Retrieval-Augmented Generation) solutions on Databricks Lakehouse architecture.
- Fine-tune and optimize open-source and proprietary LLMs using enterprise datasets.
- Develop prompt engineering frameworks and AI orchestration workflows.
- Work with Databricks Mosaic AI, MLflow, Vector Search, and Unity Catalog.
- Build and manage vector databases, embeddings pipelines, and semantic search solutions.
- Integrate AI solutions with enterprise applications, APIs, and cloud platforms.
- Optimize model performance, scalability, inference latency, and cost efficiency.
- Implement AI governance, monitoring, security, and responsible AI practices.
- Collaborate with business stakeholders, data engineers, and product teams to deliver AI solutions.
- Support AI model deployment, MLOps pipelines, and production monitoring
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What This Job Offers
Career Level
Senior
Education Level
High school or GED
