Machine Learning Engineer

Tiger Analytics; United States; On-site; USD 140,000 - 190,000; Full time

Job summary

Tiger Analytics, a global AI and analytics consulting leader, seeks a Senior Data Engineer in the United States to design scalable data pipelines and platform solutions for Fortune 1000 clients. You will apply strong Python, data processing, and software engineering practices to build reliable systems.

You will work across cloud platforms (Azure, Databricks), Spark tuning, and CI/CD pipelines, translating complex requirements into robust, observable data systems.

Qualifications

• 5+ years of professional software development with Python and OOP.
• Deep knowledge of cloud data platforms (Azure, Databricks) and Spark optimization.
• Experience building data pipelines for large-scale datasets in distributed systems.

Responsibilities

• Translate ambiguous business requirements into scalable data solutions.
• Build and optimize data pipelines and platforms.
• Collaborate with DS, DE, Cloud Ops, and Product teams.

Skills

Python

Spark

Cloud platforms

CI/CD

Cross-functional teamwork

Tools

Azure

Databricks

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.