Lead Software Engineer - Java , Kubernetics, Infrastructure

United States Digital Space LLC; Karnataka; On-site; INR 3,500,000 - 5,500,000; Full time

Job summary

United States Digital Space LLC in India (Karnataka) seeks a Lead Software Engineer to drive the design and implementation of a composable test environment platform for a large engineering organization. You will lead hands‑on development enabling isolated, production‑like environments per PR and scalable test automation across hybrid infrastructure.

The role spans Kubernetes on‑prem and AWS, traffic routing, dependency graph resolution, and stateful service provisioning.

Qualifications

• 12+ years of professional software engineering experience, with significant hands‑on experience building internal platforms, infrastructure automation, or developer tooling consumed by large engineering organizations.
• Strong hands‑on proficiency in Go, Rust, or Java for building production‑grade platform services — including API design, concurrency, fault tolerance, and operational reliability at scale.
• Deep experience with Kubernetes (on‑premises and EKS) — including namespace management, custom controllers/operators, resource scheduling, multi‑tenancy patterns, and networking.
• Hands‑on experience with traffic routing and isolation patterns — header propagation, service mesh traffic control, service discovery, and request‑scoped environment resolution.
• Strong infrastructure‑as‑code proficiency (Terraform, Ansible) with disciplined change control, state management, and repeatable provisioning.
• Proven experience provisioning and managing stateful services in automated fashion — relational databases, document stores, message brokers, and cloud‑native services.
• Experience building CI/CD pipeline extensions and integrations — embedding custom stages, webhooks, and lifecycle hooks into Jenkins, Spinnaker, GitHub Actions, or Argo CD.
• Strong systems thinking — designing for composability, multi‑tenancy, horizontal scalability, graceful degradation, and operational resilience.
• Ability to mentor engineers, lead design reviews, and drive technical quality through code review, testing standards, and documentation practices.
• Demonstrated experience designing and leading adoption of agentic AI‑enabled development practices across teams.
• Strong understanding of responsible AI use and control expectations in engineering workflows.

Responsibilities

• Designs and implement core subsystems of the composable environment platform — including the provisioning control plane, lifecycle management engine, resource scheduling, and automatic teardown with time‑to‑live enforcement
• Builds and own the traffic routing and isolation layer that directs requests to the correct service versions within virtual environment sessions using header‑based routing and service mesh integration
• Implements dependency graph resolution logic that determines which services are instantiated live, reused from baseline, or virtualized/stubbed for a given test scenario
• Develops and maintain the self‑service interfaces (CLI and API) that enable application teams to declare environment needs, provision in minutes, execute tests, and tear down
• Builds automated provisioning for stateful test infrastructure across diverse data stores (Oracle, Kafka, Cassandra, MongoDB, CockroachDB, DynamoDB, PostgreSQL) with snapshot/restore and data seeding
• Implements CI/CD pipeline integrations across Jenkins, Spinnaker, GitHub Actions, and Argo CD
• Builds observability dashboards and alerting for the platform, tracking environment spin‑up time, provisioning success, and resource utilization
• Ensures platform reliability across hybrid infrastructure with secure coding practices and governance
• Architects and governs agentic AI‑enabled engineering workflows to improve delivery speed and code quality while defining guardrails
• Drives team adoption of enterprise AI practices with secure validation and reuse of patterns across teams
• Applies SDLC tooling knowledge including enterprise‑AI capabilities to improve automation value

Skills

Go

Rust

Java

Kubernetes

Traffic routing

Service mesh

CI/CD

Architecture

Mentoring

Tools

Terraform

Ansible

Jenkins

Spinnaker

GitHub Actions

Argo CD

Istio

Linkerd

Oracle

PostgreSQL

Kafka

MongoDB

DynamoDB

Cassandra

Job description

JOB DESCRIPTION

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer at the company, within Delivery Platforms you will be a technical leader and hands‑on builder of the firm's composable test environment platform — enabling developers across a 10,000-engineer organization to spin up isolated, production-representative virtual environments per pull request, run integration tests independently, and merge without contention on shared environments. You will operate across hybrid infrastructure (on‑premises Kubernetes and AWS), solve complex problems in traffic routing, dependency graph resolution, and stateful service provisioning, and deliver a platform that thousands of engineers rely on daily.

Job Responsibilities

• Designs and implement core subsystems of the composable environment platform — including the provisioning control plane, lifecycle management engine, resource scheduling, and automatic teardown with time-to-live enforcement
• Builds and own the traffic routing and isolation layer that directs requests to the correct service versions within virtual environment sessions using header‑based routing, service mesh integration, and service discovery — ensuring zero cross‑session contamination.
• Implements dependency graph resolution logic that determines which services are instantiated live, reused from baseline, or virtualized/stubbed for a given test scenario — enabling composable environments at 1,000+ service scale
• Develops and maintain the self‑service interfaces (CLI and API) that enable application teams to declare environment needs, provision in minutes, execute tests, and tear down — with clear contracts, versioning, and backward compatibility
• Builds automated provisioning for stateful test infrastructure across heterogeneous data stores (Oracle, Kafka, Cassandra, MongoDB, CockroachDB, DynamoDB, PostgreSQL), including snapshot/restore, schema versioning, and synthetic data seeding
• Implements CI/CD pipeline integrations — environments created on PR open, tests executed, results reported, environments destroyed on merge/close — across Jenkins, Spinnaker, GitHub Actions, and Argo CD
• Builds observability, operational dashboards, and alerting for the platform itself — tracking time‑to‑environment, provisioning success rates, resource utilization, cost attribution, and isolation correctness
• Ensures the platform operates reliably across hybrid infrastructure (on‑premises Kubernetes and EKS) with consistent provisioning semantics, cross‑network connectivity, and acceptable spin‑up latency regardless of hosting tier. Mentors and provide technical guidance to staff engineers on the team, conduct design reviews, and establish coding standards and testing practices for platform code
• Architects and governs agentic AI‑enabled engineering workflows (using enterprise‑authorized tools within the work environment) to improve delivery speed, code quality, and operational outcomes at scale (e.g., AI‑driven PR review assistance, test generation/maintenance, release readiness checks, incident triage and root‑cause acceleration), while defining guardrails for validation, security, resiliency, and reuse across teams
• Drives team adoption of enterprise‑authorized AI‑assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI‑assisted code review/refactoring, test strategy acceleration, incident/root‑cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise‑authorized AI‑assisted development and automation capabilities, to improve the value realized by automation.

Required Qualifications, Capabilities, and Skills

• 12+ years of professional software engineering experience, with significant hands‑on experience building internal platforms, infrastructure automation, or developer tooling consumed by large engineering organizations
• Strong hands‑on proficiency in Go, Rust, or Java for building production‑grade platform services — including API design, concurrency, fault tolerance, and operational reliability at scale
• Deep experience with Kubernetes (on‑premises and EKS) — including namespace management, custom controllers/operators, resource scheduling, multi‑tenancy patterns, and networking (CNI, ingress, service mesh)
• Hands‑on experience with traffic routing and isolation patterns — header propagation, service mesh traffic control (Istio, Linkerd, or equivalent), service discovery, and request‑scoped environment resolution
• Strong infrastructure‑as‑code proficiency (Terraform, Ansible) with disciplined change control, state management, and repeatable provisioning across hybrid cloud and on‑premises environments
• Proven experience provisioning and managing stateful services in automated fashion — relational databases (Oracle, PostgreSQL), document stores (MongoDB, CockroachDB), message brokers (Kafka), and cloud‑native services (DynamoDB)
• Experience building CI/CD pipeline extensions and integrations — embedding custom stages, webhooks, and lifecycle hooks into Jenkins, Spinnaker, GitHub Actions, or Argo CD
• Strong systems thinking — designing for composability, multi‑tenancy, horizontal scalability, graceful degradation, and operational resilience in distributed systems
• Ability to mentor engineers, lead design reviews, and drive technical quality through code review, testing standards, and documentation practices
• Demonstrated experience designing and leading adoption of agentic AI‑enabled development practices (using enterprise‑authorized tools within the work environment) across teams, including setting standards for human‑in‑the‑loop validation, auditability/traceability of changes, and secure handling of sensitive data
• Strong understanding of responsible AI use and control expectations in engineering workflows, including security/resiliency implications, data sensitivity, and risk‑based governance; ability to influence senior technical leaders on safe scaling patterns and reuse

Preferred Qualifications, Capabilities, and Skills

• Experience building environment‑as‑a‑service platforms, composable/virtual environment systems, or namespace‑per‑PR isolation patterns at scale
• Experience implementing test data strategies at scale — masked production snapshots, on‑demand restore, synthetic data generation, and repeatable dataset management across diverse data technologies
• Experience with FinOps practices — cost attribution, chargeback modeling, idle resource detection, and automated reclamation policies
• Experience applying AI/ML techniques to infrastructure problems — predictive scaling, intelligent composition, or automated dependency resolution

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