Senior Cloud Software Engineer

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,…