Description

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.

·       Strong 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

Key Skills

Education

Bachelor's degree