Job Description: Senior Engineer – AI/ML (Databricks Certified)

We are seeking a highly skilled AI Engineer with proven experience in developing, deploying, and

optimizing AI models using the Databricks AI platform. The ideal candidate will have a strong

background in machine learning, distributed data processing, and will be a champion of Databricks

platform.

Responsibilities

 Generative AI: Experience of LLM-based solutions (LlamaIndex, LangChain, RAG

pipelines, or similar frameworks). Ability to integrate GenAI and Agentic AI into business

workflows.

 Design and implement end-to-end Generative AI solutions on Databricks, leveraging

Unity Catalog, MLflow, Delta Lake, and Vector Search

 Design, build, and deploy large-scale AI/ML models using the Databricks environment.

 Implement data validation, lineage, and monitoring using Delta Live Tables and Unity

Catalog.

 Leverage Databricks’ data engineering workflows for feature engineering, model training, and

evaluation.

 Optimize training pipelines for efficiency, scalability, and accuracy.

 Integrate AI models into production systems using APIs and microservices.

 Build reusable ML pipelines using Databricks Repos, MLflow, and Feature Store.

 Implement robust testing, monitoring, and retraining protocols for deployed models.

 Ensure adherence to compliance, security, and performance standards.

 Stay updated on advancements in AI frameworks, distributed computing, and Databricks

platform updates.

Required Skills and Qualifications

 Bachelor’s or Master’s degree in Computer Science, Data Science, or related field.

 Cerified Databricks Certified Generative AI Engineer

 Proven experience developing AI solutions on Databricks.

 Strong knowledge of Python, PySpark, MLflow, Spark and Databricks Notebooks.

 Strong understanding and knowlege of Databricks platform features such as Unity Catalog, DLT,

MosaicAI, Data Assets Bundles, etc.

 Experience with Transformer-based models, generative AI, and Databricks pipelines.

 Proficiency in integrating AI models with cloud-native architectures (AWS, Azure, or GCP).

 Solid understanding of MLOps practices, Data Assets bundles (CI/CD), and

containerization (Docker, Kubernetes) on Databricks Platform.

 Familiarity with vector databases, embeddings, and retrieval-augmented generation

(RAG).

 Strong problem-solving, analytical thinking, and communication skills.