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India
Remote only
This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Senior Backend Engineer (Go/PostgreSQL) based in India.
Join a product-focused technology environment where you will build reliable customer intelligence platforms and AI-powered workflows that solve real business challenges. As a Senior Backend Engineer, you will take ownership of complex backend problems, transforming ambiguous customer needs into scalable, production-ready solutions using Go and PostgreSQL. You will help connect data from multiple sources to create actionable insights that support revenue teams and intelligent AI agents. Working across AI integrations, multi-tenant architectures, and enterprise deployments, you will play a key role in ensuring data accuracy, system reliability, security, and performance. You will collaborate closely with product stakeholders and end users, using feedback and practical experimentation to continuously improve the customer experience. This fully remote opportunity is ideal for an experienced engineer who combines strong technical expertise with independent problem-solving and a passion for building impactful products.
Backend development and product delivery: Design, build, and improve customer-facing features within a multi-tenant backend architecture using Go and PostgreSQL. Deliver clean, maintainable, and production-ready code that supports evolving product requirements and customer needs.
Customer intelligence and data integration: Develop reliable integrations that bring together information from CRM platforms, customer conversations, support systems, and other data sources. Transform fragmented information into a unified view of customer accounts that enables teams and AI agents to make informed decisions.
AI workflow engineering: Build and maintain AI-powered workflows that analyse information, generate useful insights, and execute authorised actions. Define clear boundaries between tasks handled by large language models (LLMs) and deterministic application logic, ensuring that calculations, data storage, and business rules remain reliable and predictable.
Reliability and workflow quality: Design workflows that can be safely rerun without creating duplicate or inconsistent results. Implement appropriate validation, error handling, observability, and quality checks to detect output degradation and maintain consistent performance.
Database design and optimisation: Design and maintain PostgreSQL schemas, write and debug queries, and resolve performance bottlenecks. Ensure that data structures and database operations support efficient processing across workspaces ranging from 100 to 100,000 customer accounts.
Production operations and deployment: Support services running across AWS, Docker-based environments, enterprise infrastructure, and on-premise deployments. Investigate production issues, improve service resilience, and ensure that applications remain responsive, stable, and maintainable across different deployment environments.
End-to-end feature ownership: Take responsibility for features and services throughout their lifecycle, from understanding an initial problem and evaluating possible solutions to implementation, deployment, monitoring, and ongoing improvement. Make sound technical decisions and resolve production issues proactively.
Customer-driven product improvement: Work closely with users and product stakeholders to understand business challenges, gather feedback, and refine existing capabilities. Translate unclear requests into practical solutions that deliver measurable value and improve customer adoption.
Early delivery and onboarding: During the first 30 days, develop a strong understanding of the existing system by delivering approximately two to three fixes per week and one to two improvements to existing features, adapting priorities to the team's needs.
Strategic capability development: Within the first three to six months, take ownership of a major new capability from initial problem discovery through production release and regular customer use. Potential areas include customer intelligence pipelines, customer success management (CSM) agents, renewal automation, and expansion opportunity signals.
AI-assisted engineering practices: Use AI development tools regularly to accelerate implementation, explore solutions, improve productivity, and maintain high standards of code quality, testing, and long-term maintainability.
Professional backend engineering experience: At least seven years of backend engineering experience in product-based organisations, including hands-on experience in a startup environment. Candidates must currently be working at a product-based organisation and should understand the challenges of building and evolving customer-facing software products.
Strong Go expertise: Demonstrated experience designing, developing, and maintaining production services in Go. You should be comfortable making architectural decisions, writing maintainable code, debugging complex issues, and delivering reliable backend functionality.
PostgreSQL and database engineering: Strong practical experience designing PostgreSQL databases, developing and optimising queries, diagnosing performance problems, and making informed data modelling decisions for production systems.
Production-grade AI product development: Experience shipping a customer-facing product that uses large language models (LLMs) to make judgements or support meaningful decisions. This must extend beyond prototypes and demonstrations to a deployed product with real users and operational requirements.
End-to-end technical ownership: Proven ability to own a service, feature, or product capability from problem definition through implementation and ongoing production support. You should be comfortable investigating reliability issues, making technical trade-offs, and being accountable for outcomes.
Independent problem-solving: Ability to work through ambiguous requirements, identify the underlying customer problem, evaluate alternative approaches, and deliver practical solutions with limited supervision. A proactive, outcome-oriented mindset is essential.
AI development tools: Regular use of AI-powered tools in software development, with the ability to incorporate them effectively into coding, debugging, testing, and product development while maintaining clean, secure, and maintainable code.
Customer-focused engineering: Experience working closely with users or customers, incorporating feedback into product decisions, and improving features based on real-world usage. You should be able to balance technical quality with customer value and business priorities.
System reliability and scalability: Understanding of production operations, distributed systems, multi-tenant architectures, workflow reliability, and performance optimisation. Familiarity with AWS, Docker, enterprise deployments, and on-premise environments is valuable.
Analytical and communication skills: Strong debugging, analytical thinking, technical decision-making, and written communication skills. You should be comfortable explaining complex technical concepts, collaborating with cross-functional stakeholders, and documenting important design decisions.
Evidence of technical initiative: A public GitHub profile, personal technical project, or technical blog is a strong advantage, demonstrating practical engineering skills, curiosity, and a commitment to continuous learning.
Remote work readiness: Ability to work independently in a fully remote environment, manage priorities effectively, and take ownership of deliverables while collaborating with a distributed team.
Fully remote working arrangement from India.
Full-time contractor engagement with a US-based organisation.
Agreed compensation paid without deductions by the contracting company, as stated in the original job description.
Opportunity to build production-grade AI products and customer intelligence capabilities using Go, PostgreSQL, AWS, and modern AI technologies.
End-to-end ownership of meaningful product features, from initial problem discovery to deployment and ongoing customer use.
Exposure to complex technical challenges involving multi-tenant systems, data integrations, enterprise deployments, and AI-driven workflows.
Opportunities to work directly with product stakeholders and customers, influencing product direction and delivering practical business value.
A technically challenging environment that encourages independent decision-making, experimentation, and continuous product improvement.
Structured technical interview process comprising three to four technical rounds covering programming, system design, and AI tool usage, alongside a take-home assignment.
