Machine Learning Engineer

Aimlroles; United States; Remote; USD 130,000 - 195,000; Full time

Job summary

Tiger Analytics is seeking a senior software engineer to help build scalable data solutions across cloud platforms. The role focuses on Python development, software design principles, and delivering robust data pipelines for large datasets.

The ideal candidate will have experience with Azure/Databricks, Spark optimization, and DevOps practices to ensure reliable, production-grade systems and proactive collaboration with DS/DE/Cloud Ops teams.

Qualifications

• 5+ years of professional software development experience with Python and solid software engineering principles.

Skills

Python

Design patterns

Testing frameworks

CI/CD fundamentals

Azure

Databricks

Spark optimization

Distributed data processing

Docker

Infrastructure as code

Observability

Cross-functional collaboration

Tools

Docker

Job description

Tiger Analytics is a global leader in AI and advanced analytics consulting, empowering Fortune 1000 companies to solve their toughest business challenges. We are on a mission to push the boundaries of what AI can do, providing data-driven certainty for a better tomorrow. Our diverse team of over 6,000 technologists and consultants operates across five continents, building cutting-edge ML and data solutions at scale. Join us to do great work and shape the future of enterprise AI.

Requirements

• 5+ years of professional software development experience, with strong proficiency in Python, and applying software engineering and design principles (OOP, functional programming, design patterns, testing frameworks, CI/CD fundamentals).
• Deep understanding of cloud-based data platforms (Azure, Databricks etc.), including cluster configuration, Spark optimization techniques and best practices.
• Strong understanding of distributed data processing systems (Spark, Delta tables, cloud storage layers) with hands‑on experience in building data pipelines, optimizing performance, and handling large-scale datasets.
• Exposure to DevOps and engineering hygiene practices such as containerization (Docker), infrastructure-as-code, CI/CD pipelines, and automated testing for workflows.
• Proven ability to work effectively in cross‑functional teams (DS, DE, Cloud Ops, Product) with a proactive, inquisitive, and go‑getter mindset
• Ability to translate ambiguous business or analytical requirements into scalable technical solutions, with solid grounding in code quality, reliability, observability, and engineering best practices.

Additional qualifications (Nice to have):

• Experience in operationalizing and deploying machine learning models using production‑grade MLOps frameworks (MLflow, AzureML, Databricks Model Serving), with a strong understanding of model lifecycle management such as versioning, lineage, monitoring, retraining workflows, and deployment automation.
• Familiarity with modern data and ML architecture patterns such as feature stores, vector stores, low‑latency inference pipelines.

Benefits

Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, fast‑growing, challenging and entrepreneurial environment, with a high degree of individual responsibility.

Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.