Machine Learning Engineer

Tiger Analytics; United States; On-site; USD 140,000 - 210,000; Full time

Job summary

Tiger Analytics is seeking a seasoned software engineer/data engineer to join our AI and analytics practice in the United States. You will design and implement scalable data pipelines using Python and Spark on Azure/Databricks, optimize workflows, and contribute to MLOps; collaborate with DS, DE, Cloud Ops, and product teams to deliver production-grade data solutions.

Ideal candidates bring 5+ years of software development experience, a strong command of cloud data platforms, and a pragmatic

Qualifications

• Proficiency in Python and applying software engineering principles (OOP, FP, design patterns).
• Deep knowledge of cloud-based data platforms (Azure, Databricks) incl. cluster config and Spark optimization.
• Strong experience with distributed data processing (Spark, Delta tables, cloud storage) and building scalable data pipelines.
• Exposure to DevOps and engineering hygiene: Docker, infrastructure-as-code, CI/CD, automated testing for workflows.
• Proven ability to work in cross-functional teams (DS, DE, Cloud Ops, Product) and translate ambiguous requirements into robust solutions.

Skills

Python

Spark

Cloud platforms

Data pipelines

CI/CD

Docker

OOP & design patterns

Observability

Cross-functional teamwork

Tools

MLflow

AzureML

Databricks Model Serving

Databricks Platform

Terraform

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.