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Software Engineer (India)

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Published
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3Application actions
23 Sep 2026Apply before
Opportunity details

About this role.

AI Summary

Articul8 is hiring a senior backend software engineer to build and operate scalable infrastructure for enterprise generative AI products. The role focuses on high-availability, low-latency backend systems, event-driven architectures, APIs, microservices, and production reliability. The engineer will collaborate with product, research, engineering, and external partners while acting as an infrastructure subject-matter expert. Candidates need 7+ years of backend development experience and strong familiarity with cloud platforms, containers, databases, and modern backend frameworks.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

4/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

4/5
IndependentCollaborative
AI insightThis is a senior-level role requiring ownership of production-grade GenAI infrastructure across architecture, performance, scalability, and reliability. The breadth of cloud, backend, distributed-systems, and cross-functional responsibilities makes it technically demanding.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job medianMarket rate
$165,000
US market range$135k–$205k
AI insightNo actual salary, base-pay range, or other candidate compensation is disclosed in the posting. The figures shown are estimated annual US-market USD compensation for a senior backend/software engineer with 7+ years of experience working on cloud-native, high-scale AI infrastructure; actual pay in Brazil may differ materially based on employer, engagement structure, and local market.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you design a backend platform for real-time GenAI inference that must maintain low latency and high availability?

I would separate synchronous inference paths from asynchronous workloads, use stateless services behind load balancers, and apply autoscaling based on request, queue, and GPU utilization metrics. I would include caching, request timeouts, circuit breakers, rate limits, and graceful degradation. Observability would cover end-to-end latency, error rates, model performance, capacity, and cost.

Describe your approach to building an event-driven architecture for a production system.

I begin by defining clear domain events, ownership boundaries, delivery guarantees, and idempotency requirements. I use durable messaging, schema versioning, retry and dead-letter strategies, and consumer monitoring. I also ensure that event processing is traceable and that eventual-consistency behavior is understood by product and engineering stakeholders.

How do you decide between PostgreSQL, MongoDB, and Redis for a backend service?

I use PostgreSQL for transactional data, relational integrity, and complex querying; MongoDB when flexible document structures and rapid schema evolution are central; and Redis for low-latency caching, ephemeral state, distributed locks, or rate limiting. The decision depends on access patterns, consistency needs, operational maturity, and data lifecycle rather than technology preference alone.

What practices do you use to improve reliability and operability of microservices?

I build services with structured logging, metrics, tracing, health checks, dashboards, and actionable alerts from the start. I also use automated testing, CI/CD quality gates, infrastructure as code, safe deployment patterns, capacity planning, and incident reviews. Reliability objectives should be explicit through service-level indicators and error budgets.

How would you work with research teams to productionize a new GenAI capability?

I would align early on performance, quality, safety, security, cost, and latency requirements, then create a reproducible evaluation and deployment path. I would help package the capability behind stable APIs, establish model and prompt versioning, and add monitoring for both system health and output quality. Regular feedback loops would allow research findings and production signals to improve the product together.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.

About Us:

Articul8 was born from a simple belief: GenAI should work for the enterprise, not the other way around. Our platform — combining domain-specific models, autonomous agentic reasoning (ModelMesh™), reliable model evaluation (LLM-IQ™), and multimodal understanding — serves regulated industries such energy, semiconductor, finance, aerospace, supply chain, and more. Trusted by Fortune 500 enterprises, we bring together research, engineering, product, and domain expertise to deliver AI that meets the accuracy, explainability, and auditability standards that high-stakes environments demand.

Job Description:

Articul8 AI is seeking an exceptional Product/Software Engineer-Backend to join us in shaping the future of Generative Artificial Intelligence (GenAI). We are looking for a Product/Software Engineer-Backend with a proven track record of designing and building scalable production-level software. As a member of our Product Technology team, you will play a critical role in designing, developing, and maintaining the backend software and infrastructure supporting our GenAI-powered products. You’ll collaborate with cross-functional teams to drive innovation, optimize performance, and foster growth. This position offers exciting opportunities to work closely with cross-functional teams and external partners to drive innovations in enterprise-grade GenAI.

Responsibilities:

  • Design, develop, test, deploy, maintain, and improve scalable, secure, and high-performance backend systems, focusing on high availability, low latency, and cost-effectiveness.

  • Be the subject matter expert in infrastructure when designing new products and introducing new technology to our existing product line.

  • Collaborate closely with engineering and research teams to integrate infrastructure components with product features, ensuring optimal system performance and user experience.

  • Design event-driven architectures and develop APIs and microservices to support real-time processing and analytics.

  • Ensure system reliability, performance, and scalability through monitoring, logging, and error handling mechanisms.

  • Stay up-to-date with emerging trends, technologies, and methodologies, applying this knowledge to enhance our infrastructure capabilities.

  • Participate in code reviews, contribute to open-source projects, and mentor junior engineers.

Required Qualifications:

  • Professional experience: 7+ years of design, implementation, or consulting in applications and backend software development experience.

  • Education: BSc degree in Computer Science, Engineering, or a related field.

Preferred Qualifications:

  • Technology stack:

    • Cloud Platforms: AWS, Azure, GCP

    • Programming/ Development: Python, Node.js, Go, Ruby, CI/CD pipelines, and version control.

    • Frameworks: Django, Flask, OpenAPI, FastAPI, Spring Boot, Express.js

    • Databases: PostgreSQL, MySQL, MongoDB, Redis

    • API Management: REST, GraphQL.

    • Container Orchestration: Kubernetes, Docker Swarm

  • Education: Master’s or PhD in Computer science or related technical fields.

Professional Attributes (Code42):

  • Practice Humility: You ask questions even when you think you know the answer. You seek feedback early, learn from anyone regardless of title, and treat every experiment — especially the failures — as data.

  • Bias for Outcomes: You measure your work by what changed, not what you tried. You ship results, not slide decks. When a deadline is real, you find a way.

  • Care Deeply: You treat every problem as yours to solve. You review your own work with the rigor you’d want from a reviewer. You help teammates without being asked.

  • Dare to Do the Impossible & Embrace Scarcity: You set goals that make you uncomfortable. When told something can’t be done, you find a way or a better question. Constraints sharpen your thinking, not slow it down.

  • Build a Better World: You believe AI should make things meaningfully better for real people. You hold yourself accountable not just for whether your model works, but for what it does in the world.

Apply now >

This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

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