AI Data Engineer Jobs at Tiger Analytics with Visa Sponsorship
AI Data Engineer jobs at Tiger Analytics involve building and scaling machine learning infrastructure and data pipelines for enterprise clients across industries. The company has an established sponsorship process covering multiple visa pathways, making it a realistic target if you're on OPT, H-1B visa, or pursuing permanent residence.
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Get Access To All JobsINTRODUCTION
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 have 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.
ROLE AND RESPONSIBILITIES
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.
- Security Best Practices: In-depth understanding of security best practices in cloud environments.
- Scalability: Experience in designing and managing scalable infrastructure.
- Disaster Recovery: Knowledge of disaster recovery and business continuity planning.
- Problem-Solving: Excellent analytical and problem-solving abilities.
- Adaptability: Ability to stay up-to-date with the latest industry trends and adapt to new technologies and methodologies.
- Team Collaboration: Proven ability to work well in a team environment and contribute to a positive, collaborative culture.
GenAI Engineer Specific Skills:
- Industry Experience: 8+ years of experience in data engineering, platform engineering, or related fields, with deep expertise in designing and building distributed data systems and large-scale data warehouses.
- Data Platforms: Proven track record of architecting data platforms capable of processing petabytes of data and supporting real-time and batch ingestion processes.
- Data Pipelines: Strong experience in building robust data pipelines for document ingestion, indexing, and retrieval to support scalable RAG solutions. Proficiency in information retrieval systems and vector search technologies (e.g., FAISS, Pinecone, Elasticsearch, Milvus).
- Graph Algorithms: Experience with graphs/graph algorithms, LLMs, optimization algorithms, relational databases, and diverse data formats.
- Data Infrastructure: Proficient in infrastructure and architecture for optimal extraction, transformation, and loading of data from various data sources.
- Data Curation: Hands-on experience in curating and collecting data from a variety of traditional and non-traditional sources.
- Ontologies: Experience in building ontologies in the knowledge retrieval space, schema-level constructs (including higher-level classes, punning, property inheritance), and Open Cypher.
- Integration: Experience in integrating external databases, APIs, and knowledge graphs into RAG systems to improve contextualization and response generation.
- Experimentation: Conduct experiments to evaluate the effectiveness of RAG workflows, analyze results, and iterate to achieve optimal performance.
Benefits
This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility.
INTRODUCTION
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 have 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.
ROLE AND RESPONSIBILITIES
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.
- Security Best Practices: In-depth understanding of security best practices in cloud environments.
- Scalability: Experience in designing and managing scalable infrastructure.
- Disaster Recovery: Knowledge of disaster recovery and business continuity planning.
- Problem-Solving: Excellent analytical and problem-solving abilities.
- Adaptability: Ability to stay up-to-date with the latest industry trends and adapt to new technologies and methodologies.
- Team Collaboration: Proven ability to work well in a team environment and contribute to a positive, collaborative culture.
GenAI Engineer Specific Skills:
- Industry Experience: 8+ years of experience in data engineering, platform engineering, or related fields, with deep expertise in designing and building distributed data systems and large-scale data warehouses.
- Data Platforms: Proven track record of architecting data platforms capable of processing petabytes of data and supporting real-time and batch ingestion processes.
- Data Pipelines: Strong experience in building robust data pipelines for document ingestion, indexing, and retrieval to support scalable RAG solutions. Proficiency in information retrieval systems and vector search technologies (e.g., FAISS, Pinecone, Elasticsearch, Milvus).
- Graph Algorithms: Experience with graphs/graph algorithms, LLMs, optimization algorithms, relational databases, and diverse data formats.
- Data Infrastructure: Proficient in infrastructure and architecture for optimal extraction, transformation, and loading of data from various data sources.
- Data Curation: Hands-on experience in curating and collecting data from a variety of traditional and non-traditional sources.
- Ontologies: Experience in building ontologies in the knowledge retrieval space, schema-level constructs (including higher-level classes, punning, property inheritance), and Open Cypher.
- Integration: Experience in integrating external databases, APIs, and knowledge graphs into RAG systems to improve contextualization and response generation.
- Experimentation: Conduct experiments to evaluate the effectiveness of RAG workflows, analyze results, and iterate to achieve optimal performance.
Benefits
This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility.
See all AI Data Engineer Jobs at Tiger Analytics
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Get Access To All JobsTips for Finding AI Data Engineer Jobs at Tiger Analytics
Align your portfolio with client-facing ML work
Tiger Analytics delivers analytics consulting to enterprise clients, so project examples showing end-to-end ML pipelines, cloud data architecture, or model deployment in a business context carry more weight than academic or personal projects.
Document your specialty occupation eligibility early
AI Data Engineer roles require USCIS to confirm the position qualifies as a specialty occupation. Gather transcripts, degree certificates, and employment letters that tie your computer science or data engineering credentials directly to the technical scope of the role.
Target Tiger Analytics roles before your OPT clock runs out
If you're on F-1 OPT, factor in that H-1B cap-subject petitions are filed in April for an October start. A role accepted in late summer may not leave enough buffer, so prioritize applications several months before your OPT expiration.
Confirm the LCA wage tier matches your experience level
Before accepting an offer, check DOL's Foreign Labor Certification Data Center to verify the prevailing wage level Tiger Analytics filed for comparable AI Data Engineer positions. Level I and Level II filings can significantly affect your negotiating position.
Use Migrate Mate to surface open AI Data Engineer roles at Tiger Analytics
Searching broadly misses roles that are actively sponsoring. Migrate Mate filters specifically for visa-sponsoring employers, so you can identify Tiger Analytics openings matched to your visa type without sifting through listings that won't consider international candidates.
Ask about PERM timing if long-term residency matters to you
Tiger Analytics has sponsored EB-2 and EB-3 Green Cards for engineering roles. During the offer stage, ask whether the company initiates PERM alongside your H-1B approval or waits, since early filing can significantly reduce your overall wait given per-country backlogs.
Frequently Asked Questions
Does Tiger Analytics sponsor H-1B visas for AI Data Engineers?
Yes, Tiger Analytics sponsors H-1B visas for AI Data Engineers. The company has an active sponsorship history covering cap-subject H-1B petitions and has also supported H-1B transfers for candidates already in valid status. If you're targeting an H-1B, confirm during the offer stage whether the role is structured for a change of status or consular processing, as this affects your timeline.
Which visa types does Tiger Analytics commonly use for AI Data Engineer roles?
Tiger Analytics sponsors H-1B, TN visa, F-1 OPT, F-1 CPT, and employment-based Green Cards including EB-2 and EB-3 for AI Data Engineer positions. F-1 candidates are commonly hired through OPT or CPT arrangements before transitioning to H-1B. TN visas apply specifically to Canadian and Mexican nationals in qualifying engineering or computer science roles.
What qualifications does Tiger Analytics expect for AI Data Engineer roles?
Tiger Analytics typically looks for a bachelor's or master's degree in computer science, data engineering, or a related field, combined with hands-on experience building production-grade ML pipelines and cloud data infrastructure. Proficiency in Python, SQL, and platforms such as AWS, Azure, or GCP is expected. Consulting-style communication skills matter here because engineers work directly with enterprise clients.
How do I apply for AI Data Engineer jobs at Tiger Analytics?
You can browse and apply for AI Data Engineer roles at Tiger Analytics through Migrate Mate, which filters listings specifically for visa-sponsoring employers so you can confirm sponsorship eligibility before applying. When you apply, tailor your resume to highlight data pipeline architecture, ML model deployment, and any enterprise or consulting-facing project experience, as these align with Tiger Analytics's delivery model.
How do I plan my application timeline if I need H-1B sponsorship at Tiger Analytics?
H-1B cap-subject petitions open in March for a lottery conducted in late March, with approved petitions taking effect October 1. If you're on F-1 OPT, you need employment to start before OPT expires, so accepting an offer by February or March gives Tiger Analytics enough runway to prepare the petition. USCIS premium processing is available and can reduce approval time to 15 business days.