Senior Software Engineer

Project title - Senior Software Engineer

What you will do

Architect Data & application Systems at Scale: Design large-scale, distributed data architectures capable of processing massive datasets, integrating AI-driven workflows, and supporting real-time and batch processing needs.

Drive System Design and Execution: Architect, develop, and deploy application and data processing engine — enabling autonomous orchestration of data pipelines, analytics, and business workflows.

Lead End-to-End Productionization : Own the full lifecycle — build, containerize, deploy, and maintain apps across environments using Kubernetes, Terraform, and CI/CD systems, spark.

Define and Enforce Engineering Standards: Set best practices around code quality, observability, scalability, and operational readiness for AI and data systems.

Guide Data and Infrastructure Decisions: Lead design discussions for data modeling, system integrations, and infrastructure architecture on Google Cloud Platform (GCP) and related technologies.

Enable Observability, Traceability & Reliability: Establish end-to-end monitoring and observability frameworks (e.g., Prometheus, Grafana, OpenTelemetry) for data services, ensuring deep traceability of agentic interactions. 

Collaborate Across Teams and Domains:Partner with Product, Data Science teams to align Gen AI initiatives with data engineering objectives — ensuring production reliability and scalability. 

Mentor and Grow Engineers: Provide technical mentorship to senior and mid-level engineers, fostering a culture of innovation, ownership, and operational excellence. 

Champion Operational Efficiency: Identify and automate pain points in deployment, scaling, and monitoring. Drive continuous improvement in system reliability, performance, and developer productivity.

Own System-Level Decision Making: Evaluate new technologies, frameworks, and design patterns across LLMs  and data infrastructure to guide the organization’s technical direction.

What you will bring

• 6+ years of experience in Software Engineering, with significant focus on Data systems, integration, and distributed architectures.

• Deep expertise in Scala and distributed data frameworks like Spark data processing.

• Solid understanding of Scala or Java for high-performance and backend workloads.

• Expertise in Kubernetes (scaling, scheduling, service orchestration) and containerization practices.

• Proficiency with Terraform for cloud infrastructure provisioning and management.

• Deep understanding of CI/CD, automation, and cloud-native operations.

• Working knowledge of GCP (BigQuery, Cloud Run, Vertex AI, GCS).

• Strong experience designing for observability, monitoring, and traceability across microservices and AI agents.

• Knowledge on Airflow or equivalent for data orchestration.

• Familiarity with large language models (LLM)

• Experience with retrieval-augmented generation (RAG) systems

• Strategic mindset with the ability to balance technical depth and system-wide trade-offs.