Technical Architect (Backend)

SymphonyAI•Whitefield, NH

8d•Hybrid

About The Position

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. 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, solve ambiguous, complex technical problems, and 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.

Requirements

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)
Strong focus on code quality, testing, and automation
Comfortable using GenAI development tools (e.g., Cursor, Copilot) to improve productivity

Nice To Haves

Exposure to Data / AI platforms
Experience in working with Ontology/Knowledge Graph in any complex data ecosystem is a huge plus

Responsibilities

Design scalable, fault-tolerant distributed systems using microservices architecture
Ensure systems are built with observability, resiliency, and extensibility by design
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
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
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
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