Senior Cloud Software Engineer with ML

Visionet Systems Inc.; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Wolters Kluwer Global Business Services in Bengaluru seeks a Senior Cloud Software Engineer to own and optimize enterprise-scale AWS/Azure environments. You will design cloud-native services, RESTful APIs, and AI-enabled tooling, applying SOLID principles and policy-by-code to ensure secure, scalable cloud operations.

You will mentor teams, implement observability, and drive self-healing automation across large multi-cloud deployments.

Qualifications

• Bachelor's degree in Computer Science or a related field.
• 6+ years in Infrastructure Engineering or cloud development roles.
• Hands-on production delivery of AI/Generative AI-enabled systems.
• Experience designing or integrating agentic AI / LLM-based solutions is preferred.
• Proven ability to operate large-scale AWS and Azure environments.

Responsibilities

• Design, govern and optimize large-scale AWS and Azure environments.
• Build cloud-native services and internal platforms using Python.
• Develop secure, scalable RESTful APIs and event-driven systems.
• Create reusable libraries and shared services across engineering teams.
• Implement policy-as-code, guardrails, and pre-deployment validation.
• Own end-to-end infrastructure lifecycle and automation pipelines.
• Drive AI-enabled cloud services, including AI agents and RAG workflows.

Skills

Python

Python Frameworks

Async programming

SOLID principles

Unit testing

API Design

Cloud platforms

Cloud-native design

Serverless

Multi-cloud

Observability

Terraform

IaC tooling

Policy-as-code

CI/CD

DevSecOps

Code quality

Agile

Scrum

Stakeholder collaboration

Backlog mgmt

Agile metrics

AWS & Azure

Observability tooling

AI features

LLM tooling

MCP

RAG

Responsible AI

AIOps

Education

Bachelor's degree in Computer Science

Tools

Terraform

CloudFormation / ARM / Bicep

Ansible

Azure Functions / App Services / Logic Apps

Event Grid / Service Bus / API Gateway

Lambda / SQS / SNS / EventBridge

OpenTelemetry / Prometheus / Grafana / CloudWatch

Datadog / ELK / OpenSearch

LangChain / Semantic Kernel / Azure AI

Azure OpenAI / Amazon Bedrock

Job description

Company Overview:

Wolters Kluwer Global Business Services is designed to provide services to business units in the areas of technology, sourcing, procurement, legal, finance, and accounting, including our North American Accounting Center. These global centers promote team collaboration using best practices around a specific focus area to drive results and enhance operational efficiencies. There is a constant endeavour to benchmark against best-in-class industry standards to improve the quality of deliverables, increase cost savings, enhance productivity, and reduce time to market for products and applications

Position Overview:

The IT Infrastructure and Operations Team is a part of Wolters Kluwer Global Business Services (GBS). GBS is designed to provide services to business units in the areas of technology, sourcing, procurement, legal, finance, and accounting, including our North American Accounting Center. These global centers promote team collaboration using best practices around a specific focus area to drive results and enhance operational efficiencies. There is a constant endeavour to benchmark against best-in-class industry standards to improve the quality of deliverables, increase cost savings, enhance productivity, and reduce time to market for products and applications.

We are seeking a Senior Cloud Software Engineer with strong expertise in cloud platform engineering, automation-first operations, and intelligent CloudOps, capable of designing, governing, and optimizing large-scale AWS and Azure environments. This role requires deep technical ownership, architectural thinking, and mentoring capability, along with the ability to drive AI-assisted, policy-driven, and self-healing cloud operations.

Key Responsibilities:

Cloud Software Development

• Deep hands-on expertise in Microsoft Azure and AWS, operating at enterprise scale.
• Design, develop, test, and maintain cloud-native services and internal platforms using Python (FastAPI, Django, Flask).
• Build secure, scalable RESTful and event-driven APIs using Azure and AWS SDKs, serverless services, and managed cloud platforms.
• Develop reusable libraries, internal frameworks, and shared services that standardize cloud consumption and accelerate engineering teams.
• Apply clean architecture, SOLID principles, and enterprise design patterns to ensure maintainable and resilient solutions.
• Develop cloud services and tooling supporting large-scale Azure and AWS environments.
• Design solutions with observability, resilience, scalability, security, and cost optimization as core requirements.

AI, Generative AI & Agentic Engineering

• Design and implement AI-enabled cloud services, including intelligent automation, decision-support systems, and AI-assisted operational tooling.
• Build and integrate Generative AI and LLM-based solutions using enterprise-approved AI platforms, including retrieval-augmented generation (RAG), embeddings, vector search, and prompt/context engineering.
• Design, build, and orchestrate AI agents and multi-agent workflows that automate cloud operations, remediation, and engineering tasks.
• Integrate AI agents with enterprise systems, data sources, and tooling using the Model Context Protocol (MCP) and function/tool-calling patterns.
• Implement evaluations, guardrails, observability, cost/token controls, and human-in-the-loop feedback mechanisms to ensure reliable and secure AI usage.
• Apply Responsible AI and AI governance practices, including bias mitigation, data privacy, and protection against prompt injection and data leakage.
• Design and operate AI-driven CloudOps capabilities, including anomaly detection, predictive scaling, intelligent alerting, and alert noise reduction.
• Build self-healing and auto-remediation workflows that detect, diagnose, and resolve incidents with minimal human intervention.
• Drive incident, problem, and change management with automated runbooks, observability dashboards, and post-incident learning.

Infrastructure as Code, DevOps & Automation

• Own the end-to-end infrastructure lifecycle: design, build, operate, optimize, and decommission.
• Design and enhance Infrastructure-as-Code using Terraform with modular, reusable, organization-wide standards.
• Implement policy-as-code, guardrails, and pre-deployment validation across environments.
• Build and maintain CI/CD pipelines for cloud services and AI-enabled applications using Azure DevOps, GitHub Actions, or GitLab.
• Enable automated testing, deployment, and environment provisioning across development, non-production, and production environments.
• Embed security into the delivery lifecycle (DevSecOps), including automated scanning, dependency management, and secrets management.
• Enforce code quality standards for Cloud automation through reviews, version control, and automated validation.

Required Technical Skills & Tools:

Programming & Software Engineering

• Advanced proficiency in Python for building production-grade, cloud-native applications.
• Strong experience with web and API frameworks such as FastAPI, Django, or Flask.
• Solid understanding of asynchronous programming, background workers, and event-driven processing.
• Ability to write clean, testable, and maintainable code following SOLID principles and clean architecture patterns.
• Experience with unit testing, integration testing, and test automation frameworks.
• Working knowledge of API design standards (REST, gRPC, OpenAPI) and secure API practices (authentication, authorization, rate limiting).
• Hands-on development experience with Microsoft Azure and AWS in enterprise environments.
• Strong understanding of cloud-native application design using PaaS, serverless, and container-based services.
• Experience developing solutions using Azure Functions, App Services, Logic Apps, Event Grid, Service Bus, or equivalent AWS services (Lambda, API Gateway, SQS/SNS, EventBridge, Step Functions).
• Expertise in Cloud and Infrastructure Engineering, with a strong focus on operating, optimizing, and automating large-scale AWS and Azure environments.
• Strong understanding of Cloud operating models, including shared services, platform engineering, and multi-tenant cloud environments.
• Strong experience in hybrid and multi-cloud Cloud operations, including monitoring, governance, and operational integration.
• Experience in operational resilience engineering, including high availability, disaster recovery, backup strategy, fault tolerance, and capacity planning.

AI, Generative AI & Agentic Engineering

• Proven experience building and integrating AI-enabled features into software platforms and developer tooling.
• Hands-on experience designing agentic AI solutions and multi-agent workflows using frameworks such as LangGraph, Microsoft Agent Framework / Semantic Kernel, AutoGen, CrewAI, or the Azure AI Agent Service / AWS Bedrock Agents.
• Practical experience with the Model Context Protocol (MCP) and tool/function-calling patterns to connect AI agents to enterprise data and systems.
• Strong understanding of Generative AI concepts including prompts, context engineering, embeddings, retrieval-augmented generation (RAG), vector-based search, and chunking strategies.
• Hands-on experience with LLM orchestration and AI app frameworks (e.g., LangChain, LlamaIndex, Semantic Kernel) and AI platforms (Azure OpenAI / Azure AI Foundry, Amazon Bedrock).
• Experience working with vector databases and search (e.g., Azure AI Search, pgvector, Pinecone, Weaviate).
• Awareness of LLMOps practices such as evaluation, observability/tracing, prompt versioning, feedback loops, hallucination mitigation, guardrails, and cost/token optimization.
• Understanding of model selection trade-offs, including large and small language models, and the use of AI gateways/model routing.
• Hands-on, daily usage of AI coding assistants (e.g., GitHub Copilot) for code generation, refactoring, test creation, documentation, and productivity acceleration.
• Ability to critically review, validate, and harden AI-generated code to meet enterprise quality, security, and performance standards.
• Experience using Copilot tooling (e.g., GitHub Copilot, Microsoft 365 Copilot, Azure/Security Copilot) to improve engineering workflows, cloud operations, and developer efficiency.
• Strong understanding of Responsible AI, AI security, and governance, including prompt injection, data leakage, and secure handling of sensitive data in AI systems.
• Experience designing AIOps-driven operations, including anomaly detection, predictive analytics, intelligent alerting, and event correlation.
• Hands-on experience building self-healing and auto-remediation automation for cloud workloads.
• Strong working knowledge of observability tooling such as OpenTelemetry, Prometheus, Grafana, Azure Monitor / Application Insights, AWS CloudWatch, Datadog, or the ELK/OpenSearch stack.

Infrastructure as Code, DevOps & Automation

• Expert-level proficiency in Terraform with modular design, reusable frameworks, and organization-wide standards.
• Hands-on experience with Terraform state strategy, backend design, locking, drift detection, and remediation.
• Strong working knowledge of infrastructure and configuration automation tooling including CloudFormation, ARM/Bicep, and Ansible.
• Proficient in Python and shell scripting for developing custom Cloud automation, orchestration workflows, operational tooling, and integration scripts across cloud platforms.
• Implement policy-as-code, guardrails, and pre-deployment validation across environments.
• Ability to define and enforce code quality standards for Cloud automation through reviews, version control, and automated validation.
• Experience integrating Terraform with CI/CD pipelines for automated provisioning and deployment.

Agile and Scrum Practices

• Working knowledge of Agile and Scrum fundamentals, including Agile values, ceremonies, user stories, and sprint planning concepts.
• Hands-on experience operating within Scrum teams, actively participating in sprint planning, daily stand-ups, reviews, and retrospectives.
• Experience breaking down automation work into user stories, tasks, and backlog items with clear acceptance criteria.
• Practical exposure to iterative delivery of infrastructure, automation, and platform capabilities using Agile methodologies.
• Experience collaborating with Product Owners, Scrum Masters, and cross-functional teams to align CloudOps outcomes with business priorities.
• Familiarity with Agile metrics and practices such as velocity, sprint commitments, continuous improvement, and retrospective-driven optimization.

Relationships and engagement:

Builds strong engagement with internal teams and stakeholders by leading technical discussions, managing incidents and escalations, driving problem resolution, and implementing corrective and preventive actions. Actively contributes to knowledge sharing, lessons learned, and operational maturity improvements through continuous stakeholder feedback.

Job Qualifications & Experience

Education:

• Required: Bachelor's degree in Computer Science, or a related field

Experience

• More than 6 years of Infrastructure Engineering or cloud development roles.
• Hands-on experience delivering AI/ML or Generative AI-enabled systems in production.
• Experience designing or integrating agentic AI / LLM-based solutions is strongly preferred.
• Proven experience operating large-scale AWS and Azure environments

Other Knowledge, Skills, Abilities or Certifications:

• Certification in AWS and Azure will be prioritized

Soft Skills Required

• Strong analytical and problem-solving mindset.
• Excellent communication and cross-functional collaboration.
• Innovative approach with a focus on AI-driven automation and efficiency.
• Ownership mindset with the ability to work independently.
• Ability to collaborate effectively at all levels and functions

Seniority:

Other Duties

Performs other duties as assigned by IT Manager