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
We are looking for a 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