Introduction:
Our solutions hosted on our Iris Smart Manufacturing platform combine equipment
and process domain expertise in Mining & Metals, Oil & Gas, Chemicals &
Petrochemicals with the state-of-the-art in data sciences, machine learning, and
process optimization. The IRIS platform can work in hybrid mode and is built using
a microservices architecture. The applications are containerised and run on Azure
Kubernetes Service and use Azure blob storage, Data Lake, IoT Hub, Event Hub,
Event Grid, and Queues.
Job Description:
Job Description
We are looking for a Backend Technical Architect who combines deep Python
expertise, strong computer science fundamentals, and hands-on engineering rigor
to design and build scalable, high-performance systems.
This is a high-ownership, high-impact role where the individual is expected to:
- Operate using first-principles thinking
- Be deeply hands-on with code, not just design
- Solve ambiguous, complex technical problems
- Drive end-to-end architecture and execution
The architect will work closely with Engineering Managers, Directors, Product
Managers, and cross-functional teams to translate high-level product goals into
robust, scalable, and production-ready systems.
What Success Looks Like
- Ability to take ambiguous problem statements and convert them into clear,
executable technical plans
- Consistent delivery of high-quality, production-grade systems with strong
reliability and performance
- Demonstrated impact in eliminating bottlenecks, improving system efficiency, and
scaling platforms
- Strong influence across teams through clear technical communication and
leadership
Key Responsibilities
1. Architecture & System Design
- Design scalable, fault-tolerant distributed systems using microservices
architecture
- Ensure systems are built with observability, resiliency, and extensibility by design
2. Hands-on Development (Python-first)
- Actively contribute to core codebases with high-quality, maintainable Python code
- Lead by example in coding standards, debugging, and performance optimization
- Build reusable frameworks, libraries, and internal platforms
3. Problem Solving & Performance Engineering
- Identify and resolve system bottlenecks (CPU, memory, I/O, network, DB)
- Drive performance tuning, load testing, and scalability improvements
- Establish benchmarks and SLO-driven engineering practices
- Lead root cause analysis (RCA) and implement long-term fixes
- Design guardrails to prevent 99% of known failure modes
- Improve observability (metrics, logs, traces) across systems
4. Technical Leadership & Mentorship
- Lead a team of senior, mid-level, and junior engineers
- Mentor engineers in system design, coding best practices
- Raise the overall engineering bar across teams
5. Cross-Team Collaboration
- Work effectively with frontend, backend, data, and platform teams
- Ensure features are implemented end-to-end in a cohesive and scalable manner
- Communicate technical concepts clearly to both engineers and leadership
Required Qualifications
- Strong hands-on experience in Python (design, debugging, performance tuning)
- Experience building and scaling microservices-based distributed systems
- Deep understanding of concurrency, parallelism, async processing, and
eventdriven architectures
- Strong grasp of API design (REST/gRPC)
- Experience with data systems: SQL (Postgres) and NoSQL (MongoDB,
Elasticsearch), Messaging systems (Kafka, Event Hubs, queues), data-intensive
systems and pipelines
- Hands-on experience with cloud and infrastructure: AWS / Azure / GCP, Docker,
Kubernetes, CI/CD pipelines
• Familiarity with observability tools: Prometheus, Grafana, Kibana (or similar) •
Experience with workflow orchestration tools (Airflow, NiFi or equivalent)
- Exposure to Data / AI platforms (preferred)
- Strong focus on code quality, testing, and automation
- Comfortable using GenAI development tools (e.g., Cursor, Copilot) to improve
productivity
- Experience in working with Ontology/Knowledge Graph in any complex data
ecosystem is a huge plus
What We Offer
- Competitive salary and benefits package.
- Flexible hybrid working model.
- Opportunities for professional growth and development.
- Collaborative and inclusive work environment.
- Access to the latest technologies and tools.
- Opportunity to make a tangible impact on cutting-edge Retail/ CPG AI solutions
