Key Responsibilities:

Solution & Backend Architecture

- Define target and evolutionary architecture for backend services, integration boundaries, data flows, security controls, deployment patterns, and non- functional requirements.
- Translate product and operational capabilities into domain-aligned services, APIs, events, and reusable platform components using clean architecture and enterprise design patterns.
- Create solution designs, architecture decision records, API standards, reference implementations, and technical roadmaps that guide delivery teams.
- Evaluate technology and build-versus-buy choices for scalability, resilience, maintainability, security, performance, and total cost of ownership.
- Lead technical design reviews, identify cross-system dependencies and risks, and ensure alignment with enterprise architecture and cloud governance standards.
- Mentor developers, review critical code and designs, and improve engineering quality while maintaining delivery momentum.

Backend Services & API Engineering

- Design, develop, test, and maintain secure, production-grade backend services and APIs using Python frameworks such as FastAPI, Django, or Flask.
- Build reusable orchestration services and controlled wrappers that invoke CI/CD pipelines, automation jobs, runbooks, cloud actions, and long-running tasks.
- Implement synchronous and asynchronous processing using queues, events, workers, schedulers, callbacks, and workflow engines as appropriate.
- Establish API contracts, versioning, idempotency, validation, error handling, rate limits, auditability, and backward-compatibility practices.
- Design authorization checks and policy enforcement at service and resource levels.
- Develop integration adapters for cloud platforms, identity systems, ITSM tools, source repositories, automation platforms, and other enterprise services.

Data Layer, Integration & Synchronization

- Design logical and physical data models for configuration, service catalogs, requests, execution state, approvals, audit history, operational data, and reporting.
- Select fit-for-purpose relational, document, cache, search, and vector technologies based on access patterns, consistency, scale, security, and lifecycle needs.
- Build repositories, data-access services, and schema migration practices that separate business logic from persistence concerns.
- Design reliable ingestion and synchronization pipelines using incremental loads, change events, reconciliation, and scheduled refresh patterns.
- Define system-of-record ownership, canonical models, lineage, freshness, retention, data-quality rules, and conflict-resolution behavior.
- Implement transactional integrity, concurrency control, deduplication, retries, dead-letter handling, and recovery mechanisms for distributed workflows.
- Optimize database queries, indexes, caching, and connection management; monitor data-layer performance and capacity.

AI, Generative AI & Agentic Engineering

- Design and integrate enterprise-approved AI agents that retrieve information, recommend actions, and execute governed tools through digital workflows.
- Build secure agent tools, API adapters, and Model Context Protocol (MCP) integrations that connect AI services to enterprise data and automation capabilities.




- Implement retrieval-augmented generation using approved search or vector stores where grounded enterprise knowledge is required.
- Establish permission-aware context, human approval checkpoints, execution limits, audit trails, evaluations, observability, and fallback behavior.
- Protect AI-enabled services against prompt injection, unauthorized tool use, sensitive-data leakage, and uncontrolled cost or token consumption.

Security, Reliability & Operability

- Embed secure-by-design and DevSecOps practices, including threat modeling, secrets management, encryption, dependency scanning, and least-privilege access.
- Design for high availability, graceful degradation, timeouts, circuit breakers, retries, backpressure, disaster recovery, and operational supportability.
- Implement structured logging, metrics, distributed tracing, health checks, audit records, and service-level indicators using enterprise observability platforms.
- Build unit, integration, contract, performance, resilience, and security tests with automated quality gates in CI/CD.
- Support production incidents, lead root-cause analysis for complex failures, and convert lessons learned into architecture and engineering improvements.

DevOps & Agile Delivery

- Build and maintain CI/CD pipelines for backend services, database changes, and AI-enabled components using Azure DevOps, GitHub Actions, or equivalent tooling.
- Define cloud deployment patterns using containers, serverless services, managed application platforms, and infrastructure-as-code.
- Decompose architecture into executable user stories and tasks with clear acceptance criteria and operational readiness requirements.
- Collaborate in sprint planning, backlog refinement, reviews, retrospectives, and technical discovery while balancing delivery with architectural sustainability.

Required Technical Skills & Tools: Backend & Distributed Systems Engineering

- Advanced proficiency in Python and strong experience with FastAPI, Django, Flask, or an equivalent production API framework.
- Robust command of clean architecture, SOLID principles, domain modeling, dependency injection, concurrency, asynchronous programming, and distributed- system patterns.
- Deep knowledge of REST and OpenAPI; working knowledge of gRPC, GraphQL, or event-driven contracts where relevant.
- Experience with background workers, workflow engines, messaging, and event platforms such as Service Bus, Event Grid, SQS/SNS, EventBridge, Kafka, or equivalents.

Data Architecture & Engineering

- Strong data-modeling and SQL skills, with production experience in PostgreSQL, SQL Server, or an equivalent relational database.
- Experience with document databases, caches, and search platforms such as MongoDB, Cosmos DB, DynamoDB, Redis, OpenSearch, or Azure AI Search where justified by use cases.
- Hands-on experience with schema evolution, database migrations, query optimization, indexing, transactions,



data retention, backup, and recovery.
- Experience designing data ingestion, transformation, synchronization, reconciliation, and data-quality controls across heterogeneous sources.
- Working knowledge of vector storage and retrieval patterns for AI-enabled applications is preferred.

Cloud, Security & AI Platforms

- Hands-on architecture and development experience with Microsoft Azure and/or AWS using managed compute, API, messaging, data, identity, monitoring, and secrets-management services.
- Experience with OAuth 2.0/OIDC, service identities, role- and attribute-based access control, API gateways, and secure service-to-service communication.
- Practical experience with AI orchestration frameworks, enterprise LLM platforms, tool/function calling, MCP, retrieval-augmented generation, and AI service observability is preferred.
- Strong working knowledge of containers, Git, CI/CD, Terraform or equivalent infrastructure-as-code, and software supply-chain security.
- Hands-on use of AI coding assistants such as GitHub Copilot, with the ability to validate, test, and harden generated code and designs.

Relationships and engagement:

- Builds strong engagement with internal teams and stakeholders by leading technical discussions, clarifying requirements and dependencies, managing incidents and escalations, and driving problem resolution. Actively contributes to knowledge sharing, lessons learned, engineering standards, and continuous improvement through stakeholder feedback.

Job Qualifications & Experience

Education:

- Required: Bachelor's degree in Computer Science, Software Engineering, Information Technology, or a related field. Equivalent practical experience may be considered.

Experience

- More than 06 years of software engineering experience, including significant delivery of backend services, APIs, and data-intensive enterprise applications.
- Demonstrated experience owning solution or technical architecture while contributing hands-on production code.
- Proven experience designing relational data models and integrating or synchronizing data from multiple enterprise sources.
- Experience delivering secure, observable, and resilient services on Azure and/or AWS.
- Experience integrating automation pipelines, workflow engines, AI agents, or task-execution platforms is strongly preferred.

Other Knowledge, Skills, Abilities or Certifications:

- Microsoft Azure Developer or Solutions Architect, AWS Developer or Solutions Architect, or an equivalent cloud certification will be preferred.
- Relevant database, data engineering, architecture, security, Terraform, or AI engineering certification.

Soft Skills Required

- Strong systems-thinking and problem-solving ability across applications, integrations, data, and operations.
- Excellent communication skills with the ability to explain architecture decisions to technical and non-technical stakeholders.
- Pragmatic decision-making that balances delivery speed with security, reliability, and long-term maintainability.
- Ownership mindset with the ability to lead technical direction and work independently.
- Ability to mentor engineers, facilitate design decisions, and build alignment across teams.

Seniority: Individual Contributor

📌 Senior Cloud Software Engineer (Bengaluru)
🏢 Wipzo Systech
📍 Bengaluru