Lead Backend Engineer

Role: Lead Backend Engineer

Location: Remote, India

Experience: 8+ Years

Algoworks

About the company

Algoworks is an award-winning artificial intelligence, engineering services and experience transformation firm with offices across the United States, Europe, South America and India. We bring together a global team of engineers, architects, designers, researchers and operators united by rigor, accountability and a commitment to delivering measurable results.

For over 20 years, Algoworks has partnered with Fortune 500 organizations across the Americas, Europe and Asia to define, build and run technology that drives meaningful business outcomes. Our work combines human-centered design, engineering excellence and AI-powered capabilities to solve complex challenges with clarity and precision. Innovation, particularly in the responsible application of AI, is embedded in how teams approach problem-solving and continuous improvement.

At Algoworks, growth is continuous and closely tied to impact. Teams collaborate across geographies and disciplines, strengthening outcomes through shared insight and collective expertise. The culture values transparency, open dialogue and an environment where every voice is heard and contribution is recognized.

Through collaboration, accountability and a focus on results, Algoworks operates at the intersection of technology and people, building not only advanced systems but strong global teams that elevate performance and create lasting impact.

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Role overview

We are seeking a Lead Backend Engineer with strong production-level Python experience and deep expertise in large-scale search platforms, preferably SolrCloud/Lucene , to help integrate and scale two large healthcare data platforms.

The role will focus on enabling enriched emergency medical records from one platform to be consumed effectively by another. The platform currently manages approximately 500 TB of data and processes around 50 GB of new data daily , with the initial implementation focused on one data source and plans to onboard approximately ten additional sources.

You will work closely with a small, senior engineering team and take ownership of complex technical challenges across data ingestion, enrichment, modeling, indexing, APIs and search architecture.

Key responsibilities:

1. Data architecture and lifecycle

• Own the lifecycle of data attributes across the technology stack, from definition and modeling through ingestion, indexing, APIs and presentation.
• Define scalable data models and attribute structures to support evolving healthcare data requirements.
• Develop and maintain data ingestion and enrichment workflows using Python.
• Work closely with Java/Spring services to expose and serve enriched data through APIs.
• Collaborate with front-end teams to ensure data is effectively presented and consumed.

2. Metadata-driven framework

• Design and implement a metadata-driven framework for managing data attributes across the technology stack.
• Create a single attribute definition that can generate the required configurations and artifacts currently maintained across multiple components.
• Establish reusable patterns that simplify the introduction and management of new attributes.
• Improve consistency, maintainability and operational efficiency across the data platform.

3. Data source onboarding

• Design repeatable processes and frameworks for onboarding new data sources.
• Reduce data-source onboarding time from weeks or days to hours wherever practical through automation and metadata-driven configuration.
• Develop scalable ingestion, transformation, enrichment and indexing workflows.
• Support the initial implementation for one data source and subsequent onboarding of approximately ten additional sources.

4. Solr architecture and search performance

• Redesign the existing Solr architecture to support continued growth beyond the current fixed three-shard layout.
• Define and optimize SolrCloud sharding, routing, indexing and query strategies.
• Design solutions for high-volume indexing and low-latency search at scale.
• Diagnose and resolve search performance, indexing and scalability challenges.
• Establish best practices for Solr schema design, collections, replicas, shards and query optimization.
• Ensure the search architecture can scale reliably as data volumes and sources increase.

5. Backend engineering

• Develop scalable, maintainabl