Job Description

We are looking for a Software Engineer II to join one of our Scrum teams building AI-native, event-driven services for automotive retail intelligence. This is a core contributor role for engineers with 2–4 years of experience who are ready to take end-to-end ownership of features, grow their distributed systems skills, and work in a high velocity environment that demands code quality, hexagonal architecture discipline, and genuine curiosity about AI-native engineering.

Key Responsibilities

• Develop and maintain Java (Spring Boot 3.x) backend services — implementing domain logic, REST APIs, Kafka event consumers/producers, and MongoDB/PostgreSQL persistence adapters following hexagonal architecture patterns.
• Build features across the full backend slice: domain model, port interface, adapter implementation, unit tests, and integration tests.
• Write integration tests using Testcontainers for MongoDB, PostgreSQL, Kafka, and Elasticsearch adapters — ensuring each adapter is tested independently from the domain core.
• Participate in design discussions, architecture reviews, and code reviews — actively learning from Staff and Senior engineers.
• Investigate and resolve bugs, performance issues, and Kafka consumer lag across services.
• Write clean, well-documented, testable code with consistent application of hexagonal design principles.
• Contribute to golden dataset test scenario design under the guidance of the Product Manager and SDET.
• Operate with ownership and curiosity in a fast-paced, startup environment.

Skills and Experience

• 2–4 years of backend engineering experience; Bachelor's or Master's degree in Computer Science or a related field.
• Solid Java fundamentals: object-oriented design, Spring Boot, REST API implementation, and unit testing (JUnit 5).
• Working knowledge of at least one of: MongoDB, PostgreSQL, or Kafka — with willingness to develop depth across all three.
• Understanding of hexagonal architecture concepts (ports & adapters) — ability to implement adapters under the guidance of senior engineers.
• Familiarity with cloud environments (AWS), containerization (Docker), and CI/CD pipelines.
• Strong problem-solving skills, attention to code quality, and ability to receive and apply feedback quickly.
• Collaborative mindset and clear communication — comfortable asking questions and flagging blockers early.

Preferred Skills

• Exposure to Kafka producers and consumers in a production or learning context.
• Familiarity with Elasticsearch for search and analytics use cases.
• Curiosity about AI/ML systems — awareness of what scoring models, embeddings, and LLMs do at a systems level.
• Exposure to Redis or Aerospike for caching patterns.
  
  

Perks and Benefits

• Competitive compensation and generous stock options.
• Medical insurance coverage.
• Work with some of the brightest minds from Silicon Valley's most dominant and successful companies.

Role Overview

We are seeking a Staff Technical Program Manager to own the cross-team delivery machinery for a 20-person AI native engineering organization. This is not a project coordination role — it is a technical program ownership role.  You will maintain the master delivery plan across two Scrum teams and shared resources, track and resolve  
cross-team dependencies (platform teams, ARC backend, OEM integrations), manage risk before it becomes a miss, and create the delivery transparency that allows Engineering, Product, and Executive leadership to make fast, informed decisions. The best TPMs are invisible when things go well and indispensable when they don't. 

Key Responsibilities 

• Own the master delivery plan: maintain the integrated roadmap across both Scrum teams, shared resources (Architect, ML, SDET), and external platform dependencies — with milestone-level clarity at all times.  
• Track and resolve cross-team dependencies: Agentic Engine, Core Platform (Temporal, MCP Gateway, Component Framework), ARC backend (Kafka event contracts, API dependencies), Design (component specs), and external vendors (email/SMS providers).  
• Run the delivery rhythm: sprint review calendar, cross-team sync cadence, dependency review meetings, and risk escalation forums — keeping the Director and VP Engineering informed without burying them in status updates.  
• Identify, quantify, and manage delivery risks: surface blockers early, drive resolution with urgency, and maintain a live risk register with mitigation owners and timelines.  
• Own cross-team technical communication: translate engineering status into language that Product, Business, and Executive stakeholders can act on — and translate business urgency back into engineering trade-off decisions.  
• Drive the release process: define release criteria, coordinate go/no-go decisions with Engineering, QA (Staff SDET), and Product, and manage production deployments across services.  
• Manage the dependency interface to the T1 Agentic Engine team: track which Intent, Skill, Policy, and MCP tool registrations are ready, which are blocked, and what the critical path impact is.  
• Continuously improve the delivery system: identify bottlenecks in the engineering process, drive retrospectives, and implement changes that sustainably increase throughput and quality.

Skills & Qualifications 

• 12+ years of technical program management experience, with at least 3 years at Staff level managing multi team programs at SaaS or platform companies.  
• Strong technical depth: able to read architecture diagrams, understand Kafka dependency graphs, evaluate API contract blockers, and participate credibly in engineering design discussions.  
• Proven experience managing complex, multi-team delivery programs with many external dependencies — including platform dependencies outside your direct control.  
• Expert facilitation of engineering delivery rituals: sprint planning, dependency reviews, release gates, post mortems, and architecture syncs.  
• Deep understanding of AI-native product delivery: what makes AI system delivery different from traditional software (eval requirements, model versioning, non-deterministic outputs, golden dataset dependencies).  
• Excellent written communication: can produce a crisp, executive-readable status update that tells the truth clearly and drives decisions.  
• Strong stakeholder management across Engineering, Product, Design, and Executive levels — comfortable managing up, across, and down.  

 
Good to Have  

• Experience managing delivery programs that involve both Java microservices and Python AI/ML engineering teams simultaneously.  
• Familiarity with agentic AI platform concepts: intent registries, MCP tools, skill/policy authoring, and eval frameworks — enough to track technical dependency status accurately.  
• Background in automotive retail technology or dealer management systems.  
• Prior experience working in a US-India distributed engineering organization.  
 

Job Description

We are looking for a Senior QA Engineer / SDET to drive quality across our AI-native backend services and platform systems. You will own test automation strategy, release quality gates, and quality engineering practices — working closely with backend engineers (Java + Python), product managers, and platform architects. This role focuses heavily on validating hexagonal architecture adapters, Kafka event pipelines, multi-database correctness, and AI scoring model outputs.

Key Responsibilities

• Design, build, and scale automated test frameworks for Java microservices built on hexagonal architecture — with distinct test strategies for the domain core, adapter layers (MongoDB, PostgreSQL, Cosmos DB, Kafka, Elasticsearch, Redis/Aerospike), and external integrations.

• Develop and maintain comprehensive test suites: functional, integration (Testcontainers), regression, contract, and end-to-end across all adapter implementations.

• Validate Kafka event pipelines: consumer behavior, event ordering, idempotency, poison pill handling, and dead-letter queue processing. 

• Validate MongoDB document integrity, PostgreSQL relational consistency, Cosmos DB partition correctness, and Elasticsearch index accuracy across service boundaries.

• Design and execute golden dataset test scenarios — seeding realistic dealer and customer data to validate scoring accuracy, suppression correctness, and attribution logic.

• Validate Python-based AI/ML service outputs: scoring model results, recommendation correctness, and LLM response safety gates. 

• Collaborate closely with backend engineers and architects to embed quality early in hexagonal domain design and adapter implementation.

• Drive testability improvements by influencing API design, logging, observability, and error handling.

• Own release quality and go/no-go decisions by defining and tracking quality gates in CI/CD pipelines.

• Define and monitor quality metrics: test coverage, defect leakage, flakiness, MTTR, and automation ROI. 

• Lead root cause analysis for production issues and drive preventive measures.

• Mentor junior QA/SDET engineers and set best practices across teams.

Skills & Experience

• 6+ years of hands-on experience in QA Engineering, Test Automation, or SDET roles.

• Strong test automation expertise using modern Java-based frameworks; working knowledge of Python test frameworks (pytest) is a strong plus.

• Deep experience testing distributed microservices, REST APIs, and Kafka-based architectures.

• Experience with Testcontainers for integration testing across MongoDB, PostgreSQL, Kafka, and Elasticsearch locally.

• Strong understanding of Kafka testing: consumer behavior, event ordering guarantees, and idempotency validation.

• Experience with MongoDB and PostgreSQL testing patterns: data integrity, schema migration validation, multi-tenant isolation verification.

• Familiarity with Elasticsearch test strategies: index correctness, relevance, and query result validation.

• Experience with CI/CD pipeline integration (GitHub Actions, Jenkins, or equivalent).

• Familiarity with containerized environments (Docker, Kubernetes).

• Excellent problem-solving, communication, and collaboration skills.

Preferred Skills

• Experience validating AI/ML model outputs and scoring pipeline correctness.

• Exposure to contract testing (Pact or similar) and service virtualization for hexagonal adapter boundaries.

• Experience with performance, load, and chaos testing (JMeter, Gatling, k6) against Elasticsearch and Kafka.

• Background in DevOps or Site Reliability Engineering practices.

Perks & Benefits

• Competitive compensation and generous stock options. 

• Medical insurance coverage.

• Work with some of the brightest minds from Silicon Valley's most dominant and successful companies. 

Job Description

We are seeking a Staff SDET to own the quality architecture for our AI-native platform. This is not a test execution role — it is a quality engineering leadership role. You will design and own the test strategy across all layers of the hexagonal architecture (domain core, adapters, integration, end-to-end), establish quality gates for AI model outputs, build the golden dataset evaluation framework, and set the engineering bar for Senior SDETs and QA engineers on both Scrum teams. Nothing ships without your sign-off.

Key Responsibilities

• Own the complete quality architecture: define test strategies at each hexagonal layer — domain core unit tests, adapter integration tests (Testcontainers), service-to-service contract tests (Pact), and end-to-end scenario tests.

• Design and build the golden dataset evaluation framework: seed realistic multi-tenant dealer/customer data across MongoDB, PostgreSQL, Cosmos DB, Elasticsearch, and Kafka, and define assertion criteria for scoring accuracy, suppression correctness, and attribution logic. 

• Establish AI quality gates: define evaluation criteria for ML model outputs (scoring, recommendations), LLM response quality, RAG retrieval accuracy (precision@k, recall), and agentic workflow correctness.

• Architect test infrastructure: Testcontainers configurations for MongoDB, PostgreSQL, Cosmos DB, Kafka, Elasticsearch, and Redis/Aerospike — enabling fully local integration test runs.

• Own CI/CD quality pipeline: define what automated tests run at commit, PR, staging, and pre-release gates — with blocking quality thresholds.

• Define and track quality metrics: test coverage per hexagonal layer, defect leakage, flakiness rate, MTTR, and automation ROI.

• Lead root cause analysis for production escapes — own post-mortems and preventive measure implementation. 

• Mentor Senior SDETs and QA engineers across both teams; conduct design reviews for test frameworks and golden dataset designs.

• Influence backend and frontend engineers on testability: drive API design, logging, observability, and error handling that makes services intrinsically testable. 

Skills & Experience

• 8+ years of QA Engineering, Test Automation, or SDET experience with strong hands-on engineering depth.

• Deep expertise designing test strategies for hexagonal architecture: proven ability to test domain cores independently from adapters, and adapters independently from each other.

• Strong programming skills in Java (primary) and Python — able to write production-quality test frameworks, not just test scripts. 

• Expert-level Testcontainers experience across MongoDB, PostgreSQL, Kafka, Elasticsearch, and Redis/Aerospike. 

• Experience with contract testing (Pact or similar) for service boundary validation and service virtualization.

• Deep Kafka testing expertise: consumer group behavior, event ordering guarantees, idempotency, and dead-letter queue validation.

• Experience designing golden dataset strategies: multi-tenant data seeding, realistic event sequences, and deterministic assertion design.

• Experience integrating quality gates into CI/CD pipelines with meaningful blocking thresholds.

• Strong understanding of AI/ML output validation: scoring correctness, RAG retrieval quality metrics, and agentic workflow eval design.

Preferred Skills

• Experience with performance, load, and chaos testing (Gatling, k6) against Kafka, Elasticsearch, and MongoDB at scale.

• Background in AI/ML quality engineering: eval frameworks (RAGAS or similar), model regression detection, and LLM safety testing.

• Prior experience owning quality for a platform serving multiple engineering teams (quality platform ownership).

• Familiarity with Cosmos DB testing patterns and eventual consistency validation strategies.

Perks & Benefits

• Competitive compensation and generous stock options.

• Medical insurance coverage.

• Work with some of the brightest minds from Silicon Valley's most dominant and successful companies.

Job Description

We are looking for a Senior QA Engineer / SDET to drive quality across our AI-native backend services and platform systems. You will own test automation strategy, release quality gates, and quality engineering practices — working closely with backend engineers (Java + Python), product managers, and platform architects. This role focuses heavily on validating hexagonal architecture adapters, Kafka event pipelines, multi-database correctness, and AI scoring model outputs.

Key Responsibilities

• Design, build, and scale automated test frameworks for Java microservices built on hexagonal architecture — with distinct test strategies for the domain core, adapter layers (MongoDB, PostgreSQL, Cosmos DB, Kafka, Elasticsearch, Redis/Aerospike), and external integrations.

• Develop and maintain comprehensive test suites: functional, integration (Testcontainers), regression, contract, and end-to-end across all adapter implementations.

• Validate Kafka event pipelines: consumer behavior, event ordering, idempotency, poison pill handling, and dead-letter queue processing. 

• Validate MongoDB document integrity, PostgreSQL relational consistency, Cosmos DB partition correctness, and Elasticsearch index accuracy across service boundaries.

• Design and execute golden dataset test scenarios — seeding realistic dealer and customer data to validate scoring accuracy, suppression correctness, and attribution logic.

• Validate Python-based AI/ML service outputs: scoring model results, recommendation correctness, and LLM response safety gates. 

• Collaborate closely with backend engineers and architects to embed quality early in hexagonal domain design and adapter implementation.

• Drive testability improvements by influencing API design, logging, observability, and error handling.

• Own release quality and go/no-go decisions by defining and tracking quality gates in CI/CD pipelines.

• Define and monitor quality metrics: test coverage, defect leakage, flakiness, MTTR, and automation ROI. 

• Lead root cause analysis for production issues and drive preventive measures.

• Mentor junior QA/SDET engineers and set best practices across teams.

Skills & Experience

• 6+ years of hands-on experience in QA Engineering, Test Automation, or SDET roles.

• Strong test automation expertise using modern Java-based frameworks; working knowledge of Python test frameworks (pytest) is a strong plus.

• Deep experience testing distributed microservices, REST APIs, and Kafka-based architectures.

• Experience with Testcontainers for integration testing across MongoDB, PostgreSQL, Kafka, and Elasticsearch locally.

• Strong understanding of Kafka testing: consumer behavior, event ordering guarantees, and idempotency validation.

• Experience with MongoDB and PostgreSQL testing patterns: data integrity, schema migration validation, multi-tenant isolation verification.

• Familiarity with Elasticsearch test strategies: index correctness, relevance, and query result validation.

• Experience with CI/CD pipeline integration (GitHub Actions, Jenkins, or equivalent).

• Familiarity with containerized environments (Docker, Kubernetes).

• Excellent problem-solving, communication, and collaboration skills.

Preferred Skills

• Experience validating AI/ML model outputs and scoring pipeline correctness.

• Exposure to contract testing (Pact or similar) and service virtualization for hexagonal adapter boundaries.

• Experience with performance, load, and chaos testing (JMeter, Gatling, k6) against Elasticsearch and Kafka.

• Background in DevOps or Site Reliability Engineering practices.

Perks & Benefits

• Competitive compensation and generous stock options. 

• Medical insurance coverage.

• Work with some of the brightest minds from Silicon Valley's most dominant and successful companies.