Machine Learning Engineer - United States

Tiger Analytics, LLC; United States; Remote; USD 140,000 - 200,000; Full time

Job summary

Tiger Analytics is a global leader in AI and analytics consulting, helping Fortune 1000 companies solve complex business challenges with scalable ML and data solutions.

This role requires 5+ years in software development, strong Python, cloud platforms (Azure/Databricks), and hands-on experience with data pipelines, Spark optimization, and CI/CD.

Join a fast-growing team that values engineering excellence, cross-functional collaboration, and impact at scale.

Qualifications

• 5+ years of professional software development experience focusing on Python.
• Deep expertise in cloud-based data platforms and Spark optimization.
• Experience in building scalable data pipelines and large datasets.
• Familiarity with DevOps practices, containerization, and testing.

Skills

Python programming

CI/CD fundamentals

Spark optimization

Cloud data platforms

Distributed data processing

Cross-functional collaboration

Tools

Python

Azure

Databricks

Spark

Delta tables

Docker

MLflow

AzureML

Databricks Model Serving

Feature stores

Vector stores

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.

• 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.

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.