Senior AI Platform Software Engineer

Insight Global; Hyderabad; On-site; INR 4,500,000 - 7,000,000; Full time

Job summary

Insight Global seeks a Senior Software Engineer to lead the architecture and development of AI-powered operational systems that support large-scale developer workflows and knowledge management.

You will own the technical direction of AI-enabled platform capabilities, drive engineering standards, and mentor distributed teams to build scalable systems leveraging Generative AI for improved efficiency and user experience.

Qualifications

• 10+ years of software engineering experience building large-scale backend platforms or enterprise software.
• Proficiency in Python, Java, Go, or Scala, with Python preferred.
• Hands-on experience deploying Generative AI solutions into production.
• Experience with LLM integrations, vector databases, evaluation pipelines, and AI observability.
• Strong cloud-native architecture knowledge: APIs, microservices, event-driven systems, databases, caching, monitoring, tracing.

Responsibilities

• Own architecture, roadmap, and engineering strategy for AI-native platforms.
• Design scalable solutions for conversational support, knowledge management, and workflow automation.
• Improve AI-powered user experiences with contextual, low-noise responses.
• Lead production AI capabilities (RAG, hybrid search, LLM integrations, safety controls).
• Establish engineering standards for reliability, observability, and platform scalability.
• Mentor engineers through architecture/code reviews and troubleshooting.
• Automate recurring patterns into reusable platform capabilities.

Skills

Distributed systems

Backend programming (Python/Java/Go/SC

Generative AI production

LLM integrations

Cloud-native architecture

Education

Bachelor’s or Master’s in CS/Engineering

Tools

AKS

Grafana

ArgoCD

GitLab

PagerDuty

Vector databases

Job description

Insight Global is seeking a Senior Software Engineer to lead the architecture, development, and evolution of AI-powered operational systems that enable large-scale developer support, workflow automation, knowledge management, and service intelligence.

This role combines hands-on software engineering with technical leadership. You will own the technical direction of AI-enabled platform capabilities, drive engineering standards, mentor distributed teams, and build scalable systems that leverage Generative AI to improve operational efficiency and user experience.

The ideal candidate brings deep expertise in distributed systems, backend platform engineering, cloud-native technologies, and production-grade AI applications. You understand that successful AI products require not only accuracy, but also reliability, observability, security, scalability, and operational trust.

Key Responsibilities

• Own the architecture, technical roadmap, and engineering strategy for AI-native operational platforms and intelligent assistant capabilities.
• Design and build scalable solutions supporting conversational support, knowledge management, workflow automation, incident response, service intelligence, and operational analytics.
• Improve AI-powered user experiences by delivering contextual, actionable, and low-noise responses.
• Lead the implementation of production AI capabilities including:
• Retrieval-Augmented Generation (RAG)
• Hybrid search architectures
• LLM integrations
• Evaluation and testing frameworks
• Prompt management and versioning
• AI guardrails and safety controls
• Observability and hallucination detection
• Design and develop backend services and integrations with enterprise platforms such as collaboration tools, ticketing systems, incident management platforms, source control systems, developer tools, monitoring systems, and cloud services.
• Establish and enforce engineering standards for system reliability, observability, release quality, operational readiness, and platform scalability.
• Mentor engineers through architecture reviews, code reviews, troubleshooting, and technical leadership.
• Identify operational inefficiencies and transform recurring patterns into automated workflows and reusable platform capabilities.

Required Qualifications

• 10+ years of software engineering experience building and operating large-scale backend platforms, distributed systems, developer platforms, automation platforms, or enterprise software solutions.
• Strong programming expertise in one or more modern backend languages such as Python, Java, Go, or Scala, with Python preferred.
• Hands‑on experience deploying Generative AI solutions into production environments.
• Practical experience with:
• LLM integrations
• Vector databases and semantic search
• Evaluation pipelines
• AI observability
• Guardrails and governance
• Workflow automation
• Deep knowledge of cloud‑native architecture and distributed systems, including:
• APIs and microservices
• Event‑driven architectures
• Databases and caching layers
• Monitoring and telemetry
• Distributed tracing
• Strong understanding of reliability and resiliency patterns such as retries, idempotency, rate limiting, circuit breakers, failure isolation, asynchronous processing, and incident response.
• Experience designing secure enterprise integrations with an emphasis on authentication, authorization, auditability, governance, and operational safety.
• Demonstrated ability to influence technical direction, drive architectural decisions, mentor engineers, and lead execution across globally distributed teams.
• Excellent written and verbal communication skills.
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

Preferred Qualifications

• Experience in platform engineering, developer productivity, site reliability engineering (SRE), cloud infrastructure, enterprise automation, or large‑scale SaaS environments.
• Experience building AI‑powered support systems, operational intelligence platforms, workflow orchestration solutions, incident response tooling, or developer‑facing assistants.
• Knowledge of conversational interfaces, support automation, ticketing workflows, incident management workflows, and human‑in‑the‑loop AI systems.
• Experience with technologies such as:
• AKS
• Grafana
• ArgoCD
• GitLab
• PagerDuty
• Experience collaborating with engineering teams across multiple geographic regions and time zones.
• Strong systems thinking and a demonstrated ability to simplify complex architectures while maintaining scalability and reliability.

What Success Looks Like

• Deliver scalable AI‑powered operational capabilities that improve developer productivity and service efficiency.
• Establish robust engineering standards and best practices across platform development.
• Drive adoption of AI‑powered automation while maintaining high levels of security, reliability, and operational trust.
• Mentor and elevate engineering teams through technical leadership and architectural excellence.
• Build intelligent systems that transform operational data and workflows into actionable insights and automated outcomes.