Lead Backend Engineer

SymphonyAI•Whitefield, NH

16h•Hybrid

About The Position

SymphonyAI is seeking a hands-on technical architect to design and build scalable, high-performance systems for its Iris Smart Manufacturing platform. The platform combines industry expertise with data science, machine learning, and process optimization; it uses a hybrid-capable microservices architecture, with containerized applications running on Azure Kubernetes Service and Azure data and messaging services. The role focuses on applying deep Python expertise and strong computer science fundamentals to ambiguous, complex technical problems. The successful candidate will drive architecture and execution end to end, contribute directly to production code, and work with engineering managers, directors, product managers, and cross-functional teams to turn product goals into reliable, scalable systems. Success includes creating clear technical plans, delivering high-quality production systems, removing bottlenecks, improving efficiency, and influencing teams through effective technical leadership.

Requirements

4 to 7 years of hands-on Python experience, including design, debugging, and performance tuning.
Experience building and scaling microservices-based distributed systems.
Deep understanding of concurrency, parallelism, asynchronous processing, and event-driven architectures.
Strong grasp of REST or gRPC API design.
Experience with SQL databases such as Postgres and NoSQL systems such as MongoDB and Elasticsearch.
Experience with messaging systems such as Kafka, Event Hubs, and queues, and with data-intensive systems and pipelines.
Hands-on experience with AWS, Azure, or GCP; Docker; Kubernetes; and CI/CD pipelines.
Familiarity with observability tools such as Prometheus, Grafana, and Kibana, or similar tools.
Experience with workflow orchestration tools such as Airflow, NiFi, or equivalent.
Strong focus on code quality, testing, and automation.
Comfort using GenAI development tools such as Cursor or Copilot to improve productivity.

Nice To Haves

Exposure to Data or AI platforms.
Experience with Ontology or Knowledge Graph in a complex data ecosystem.

Responsibilities

Design scalable, fault-tolerant distributed systems using microservices architecture.
Build observability, resiliency, and extensibility into systems by design.
Contribute hands-on to core codebases with high-quality, maintainable Python code.
Set an example in coding standards, debugging, and performance optimization.
Build reusable frameworks, libraries, and internal platforms.
Identify and resolve CPU, memory, I/O, network, and database bottlenecks.
Drive performance tuning, load testing, and scalability improvements.
Establish benchmarks and SLO-driven engineering practices.
Lead root cause analysis and implement long-term fixes.
Design guardrails to prevent known failure modes.
Improve system observability through metrics, logs, and traces.
Lead a team of senior, mid-level, and junior engineers.
Mentor engineers in system design and coding best practices, and raise engineering standards across teams.
Collaborate with frontend, backend, data, and platform teams to deliver cohesive, scalable features end to end.
Communicate technical concepts clearly to engineers and leadership.