Software Engineer Java + Data (PySpark)

Lineate is a US-based international software development company with over two decades of experience.

From Intelligent Document Processing(IDP) and Agentic RAG systems to scalable cloud architectures, we turn complex ideas into real, measurable results.

We deliver AI-driven custom solutions for FinTech, HealthTech, AdTech, and beyond, empowering businesses to grow smarter, faster, and more efficiently.

Our expertise falls into three main categories:

• Building Custom AI Solutions: Deploying high-impact, AI-enabled technology utilizing IDP, Agentic RAG.
• Cloud and Data Infrastructure: Optimizing business operations with our data management and cloud computing solutions.
• Team Augmentation: Providing specialized experts in FinTech, AdTech, and HealthTech to integrate seamlessly and accelerate project timelines.
• Our goal is not just to build technology, but to build the future operating model for our clients.

• →Design, develop, and maintain scalable backend services using Java and Python
• →Build and optimize data processing pipelines and APIs for high-performance applications
• →Collaborate with cross-functional teams to deliver reliable and efficient solutions
• →Improve system performance, scalability, and reliability
• →Work with large datasets to support search, recommendation, or ML-driven features
• →Contribute to architecture decisions and technical design
• →Write clean, maintainable, and well-documented code

• 6+ years of commercial software development experience
• Strong hands-on experience with both Java and Python (primarily PySpark code)
• Experience in designing, developing, and optimizing scalable data processing pipelines and backend APIs for high-performance applications
• Solid understanding of backend development principles and system design
• Experience working with APIs, microservices, and distributed systems

• Databricks OR AWS EMR OR Hadoop
• Search technologies experience, such as:

Lexical search (e.g., Solr, Elasticsearch)

Semantic search, vector search, or RAG-based systems

Search relevance tuning and optimization

• Machine Learning experience, especially in:

Recommendation systems

User behavior prediction (e.g., click-through rate prediction, relevance estimation)

Practical ML application in production systems