Job Summary
highly skilled Technical Lead to build and scale cloud-native backend systems on Azure. This role combines hands-on engineering, technical leadership, and innovation in Generative AI to deliver high-impact solutions for global clients.
The ideal candidate is a proactive leader who can balance deep technical expertise with team leadership, drive architectural decisions, and ensure high-quality delivery while collaborating with internal and external stakeholders.
Key Responsibilities
Technical Leadership
•
Lead and mentor a cross-functional engineering team (5–7 engineers), ensuring high standards in code quality and delivery
•
Own backend architecture, system design, and engineering roadmap
•
Drive end-to-end design, development, and deployment of scalable systems
•
Conduct code reviews, enforce best practices, and resolve complex technical challenges
•
Drive internal R&D initiatives in GenAI, automation, and scalable cloud solutions
Backend Engineering
•
Design and build high-performance, scalable, and secure REST APIs using FastAPI
•
Develop microservices and distributed systems with fault tolerance and resiliency
•
Optimize systems for latency, scalability, and reliability
•
Implement best practices for:
o
Performance optimization
o
Security
o
Testing (unit/integration)
o
Observability (logging, metrics, tracing)
Cloud & DevOps (Azure)
•
Architect and deploy cloud-native solutions using:
o
Compute: Azure App Services, Container Apps, AKS, Azure Functions
o
Data: Azure SQL, Cosmos DB, Storage Accounts
o
Monitoring: Application Insights, Log Analytics
•
Build and manage CI/CD pipelines using Azure DevOps
•
Implement Infrastructure-as-Code using Terraform (preferred) or Bicep
•
Ensure:
o
High availability and fault tolerance
o
Monitoring, alerting, and observability
o
Cost optimization and governance
Client & Stakeholder Management
•
Act as the primary technical point of contact for internal clients
•
Lead technical discussions, solution design workshops, and architecture reviews
•
Translate business requirements into scalable technical solutions
•
Communicate risks, trade-offs, and progress effectively
•
Present technical concepts clearly to both technical and non-technical stakeholders
Generative AI (Preferred)
•
Design and implement RAG-based architecture and LLM-powered services
•
Work with:
o
Azure OpenAI / OpenAI APIs
o
Vector databases (e.g., Azure AI Search)
•
Build production-grade GenAI applications focusing on scalability, latency, and security
•
Evaluate and integrate emerging AI technologies into client solutions
Leadership & Soft Skills
•
Communication: Clearly explains technical decisions, trade-offs, and risks; leads client discussions confidently
•
Team Leadership: Mentors engineers, provides actionable feedback, and maintains high engineering standards
•
Ownership: Takes end-to-end responsibility for delivery, production issues, and system quality
•
Decision-Making: Makes sound technical decisions under ambiguity, balancing short-term needs and long-term design
•
Execution: Drives predictable delivery, proactively manages risks, and ensures high-quality outcomes
•
Collaboration: Works effectively across engineering, product, and business teams
•
Adaptability: Quickly learns, adopts new technologies (especially GenAI), and drives continuous improvement
Skill Requirements
8–12 years in core software development, distributed systems, or large-scale platform engineering - including strong Python and Nodejs expertise.
•
4-5 years building, deploying, and scaling machine learning models, LLMs, or agentic workflows into live production.
•
Deep experience with FastAPI (or similar modern frameworks)
•
Hands-on experience with Azure cloud architecture and operations
•
Strong knowledge of:
o
Microservices and distributed systems
o
Docker and Kubernetes (ACA and AKS)
•
Proven experience with:
o
CI/CD pipelines (Azure DevOps)
o
Infrastructure as Code (Terraform preferred)
•
Demonstrated leadership experience in mentoring teams and driving delivery
Preferred Skills
•
Experience with Azure AI foundry, Databricks, or big data pipelines
•
Knowledge of event-driven systems (Azure Service Bus, Azure Data Factory)
•
Familiarity with API gateways and service mesh
•
Understanding of cloud security, IAM, and governance