Senior Generative AI Engineer (Databricks,Data Lake) II 100% Remote

Title: Senior Generative AI Engineer (Databricks,Data Lake)

Location: 100% Remote

Job Type: Fulltime

Key Responsibilities

• Design and implement

  GenAI models

  (LLMs, multimodal, embeddings, and fine-tuning) for enterprise use cases.
• Architect and optimize

  data pipelines and workflows

  in Databricks for large-scale AI training and inference.
• Manage and maintain

  data lakes

  on Databricks, ensuring data quality, governance, and scalability for AI workloads.
• Collaborate with

  data scientists, ML engineers, and product teams

  to translate business problems into AI-driven solutions.
• Implement

  MLOps pipelines

  for model deployment, monitoring, and lifecycle management in Databricks.
• Explore and evaluate

  state-of-the-art GenAI techniques

  and integrate them into scalable architectures.
• Optimize performance, scalability, and cost-effectiveness of AI systems in

  cloud environments (AWS/Azure/GCP)

  .
• Contribute to technical strategy, mentorship, and best practices in

  AI and data engineering

  .

Required Qualifications

• 8+ years

  of professional experience in

  AI/ML engineering, data engineering, or related roles

  .
• Strong expertise with

  Databricks platform

  , including:
• Databricks Workflows & Delta Lake
• Databricks ML Runtime & MLflow
• Databricks SQL
• Hands-on experience designing and managing

  data lakes

  at enterprise scale.
• Proficiency in

  Python

  (PySpark, Pandas, ML/AI libraries) and

  SQL

  .
• Solid background in

  Machine Learning, Deep Learning, and Generative AI frameworks

  (e.g., Hugging Face, LangChain, OpenAI APIs, TensorFlow, PyTorch).
• Experience with

  MLOps practices

  , CI/CD pipelines, and cloud-native deployments.
• Strong knowledge of

  cloud platforms (AWS, Azure, or GCP)

  and data services.
• Excellent problem-solving skills and ability to design scalable AI solutions for complex datasets.

Preferred Qualifications

• Databricks certifications

  (e.g., Databricks Certified Data Engineer Professional, Machine Learning Professional).
• Experience with

  vector databases

  (Pinecone, Weaviate, FAISS, Chroma).
• Experience with

  fine-tuning LLMs

  and integrating RAG (Retrieval-Augmented Generation) pipelines.
• Background in

  data governance, lineage, and compliance

  in large-scale AI systems.
• Contributions to

  open-source AI/ML projects

  or publications in the AI community