Sr. AI Engineer with Databricks experience

What is in it for you

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

Roles and 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

Experience: -

8 Years

Location: -

Irving TX (Hybrid 3 days WFO)

Duration: -

C2C

Educational Qualifications: -

Engineering Degree BE/ME/BTech/MTech/BSc/MSc.

Technical certification in multiple technologies is desirable.

Skills: -

Mandatory skills

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.

Preferred Qualifications:

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. Technologies & Tools Databricks Mosaic AI MLflow Delta Lake Python SQL PyTorch TensorFlow OpenAI API Hugging Face LangChain LangGraph Vector databases: Pinecone FAISS ChromaDB AWS Azure GCP Docker Kubernetes GitHub Actions