Gen AI Data Engineer - Tiger Analytics

Tiger Analytics is looking for experienced Machine Learning Engineers with Gen AI experience to join our fast-growing advanced analytics consulting firm. Our employees bring deep expertise in Machine Learning, Data Science, and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner.

We are looking for top-notch talent as we continue to build the best global analytics consulting team in the world. You will be responsible for:

Technical Skills Required:

Programming Languages: Proficiency in Python, SQL, and PySpark.

Data Warehousing: Experience with Snowflake, NOSQL and Neo4j.

Data Pipelines: Proficiency with Apache Airflow.

Cloud Platforms: Familiarity with AWS (S3, RDS, Lambda, AWS batch, SageMaker processing Job, CloudFormation, etc.) or GCP (Vertex AI RAG, Data pipeline, Bigquery, GKE)

Operating Systems: Experience with Linux.

Batch/Realtime Pipelines: Experience in building and deploying various pipelines.

Version Control: Experience with GitHub.

Development Tools: Proficiency with VS Code.

Engineering Practices: Skills in testing, deployment automation, DevOps/SysOps.

Communication: Strong presentation and communication skills.

Collaboration: Experience working with onshore/offshore teams.

Requirements

Desired Skills:

• Big Data Technologies: Experience with Hadoop and Spark.

Data Visualization: Proficiency with Streamlit and dashboards.

• APIs: Experience in building and maintaining internal APIs.
• Machine Learning: Basic understanding of ML concepts.
• Generative AI: Familiarity with generative AI tools and techniques.

Additional Expertise:

• Knowledge Graphs: Experience with creation and retrieval.
• Vector Databases: Proficiency in managing vector databases.
• Data Persistence: Ability to develop and maintain multiple forms of data persistence and retrieval methods (RDMBS, Vector Databases, buckets, graph databases, knowledge graphs, etc.).
• Cloud Technologies: Experience with AWS, especially SageMaker, Lambda, OpenSearch.
• Automation Tools: Experience with Airflow DAGs, AutoSys, and CronJobs.
• Unstructured Data Management: Experience in managing data in unstructured forms (audio, video, image, text, etc.).
• CI/CD: Expertise in continuous integration and deployment using Jenkins and GitHub Actions.
• Infrastructure as Code: Advanced skills in Terraform and CloudFormation.
• Containerization: Knowledge of Docker and Kubernetes.
• Monitoring and Optimization: Proven ability to monitor system performance, reliability, and security, and optimize them as needed.
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