Senior Backend Engineer (Python, Java, GO)
Who We Are
Back in 2019, spiderSilk was born with a bold idea: build regional, sovereign cybersecurity IP that could stand tall on the global stage. Our mission is to shake up the way organizations protect their ever-changing digital worlds — with continuous, intelligent, and autonomous security that doesn't miss a beat.
We're a global mix of curious minds, problem-solvers, and passionate builders, all united by one goal: making the internet a safer place for everyone. Around here, we thrive on vision, energy, and a strong sense of ownership.
If this feels like your kind of crew, you'll probably fit right in.
About the Role
As a Senior Software Engineer (Backend), you'll take a leadership role in designing and building the mission-critical systems that power spiderSilk's cybersecurity platform. You'll architect scalable, secure, and intelligent backend infrastructure — integrating ML pipelines, designing agentic AI solutions, and ensuring the platform can handle the demands of real-world threat intelligence at scale.
You won't wait to be told what to do. You'll jump into architectural discussions, challenge assumptions, and drive decisions. You'll mentor junior engineers and set the bar for engineering quality across the team.
What You'll Do
Architect and maintain distributed, microservices-based systems that operate reliably at scale
Own the design and delivery of standalone agentic AI solutions — from architecture through to production deployment
Build and optimise ML pipelines, agent workflows, and vector-based similarity search systems
Design and operate event-driven architectures using Kafka and message queues for real-time data streaming
Engineer and optimise complex database schemas across relational, graph, search, and analytical data stores
Lead data pipeline development for ingesting, processing, and surfacing threat intelligence data
Proactively contribute to architectural decisions — bringing opinions, trade-off analysis, and technical leadership without waiting to be asked
Mentor junior and mid-level engineers through code reviews, design discussions, and pair programming
Champion secure-by-design engineering practices across every layer of the stack
Requirements
Languages & Stack
Python, Go, and Java — strong proficiency in all three with production-grade backend experience across each
Deep understanding of concurrency primitives — threads, goroutines, thread pools, locks, and race conditions; knowing when more parallelism makes things slower, not faster
Solid working knowledge of Docker and Kubernetes for containerisation and orchestration
Experience with Redis for caching, rate limiting, and distributed coordination
Distributed Systems & Architecture
Deep experience designing and operating distributed systems and microservices architectures
Proven track record building event-driven systems (Kafka, RabbitMQ, or equivalent)
Expertise in data pipelines and stream processing at scale
Strong system design instincts — able to reason independently about trade-offs across reliability, performance, and security, and step into architectural decisions without prompting
Hands-on experience with wide-column stores (Cassandra or equivalent) for high-write, high-scale workloads
Working knowledge of object storage (S3 or compatible) and workflow orchestration tools (Dagster, Airflow, or similar) for managing complex data pipelines
AI / Agentic Systems
Hands-on experience building standalone agentic AI solutions in production — not just integrating APIs, but designing the agent architecture end-to-end
Deep understanding of vector databases and similarity search (e.g. Pinecone, Weaviate, pgvector, Qdrant) and how to apply them effectively in AI-powered pipelines
Proven work on agent optimisation and performance tuning — latency reduction, retrieval quality, context management, tool-call efficiency
Familiarity with agentic frameworks (LangChain, LlamaIndex, AutoGen, or similar) and the ability to go beyond them when needed
Practical experience with ML pipeline design, model serving, and feature engineering within production AI systems
Strong prompt engineering skills and up-to-date fluency with the evolving AI tooling ecosystem
Expert-level skills in database schema design and optimisation across multiple paradigms
MySQL & PostgreSQL — advanced query optimisation, indexing strategies, performance tuning, and schema design for high-traffic systems
Elasticsearch — indexing, search relevance tuning, and cluster operations
Graph databases — production experience with Neo4j or equivalent (Amazon Neptune, ArangoDB); ability to model complex relationship data for threat intelligence or entity resolution
Working knowledge of analytical / OLAP databases (e.g. ClickHouse, BigQuery, Redshift, DuckDB) and when to apply them
Ability to select the right database technology for the problem and design schemas that scale
Data Collection & Web Technologies
Experience collecting data from the web at scale — HTTP clients, headless browsers (Playwright or similar), handling sessions, rate limits, and sources that change without notice
Ability to parse large, malformed, or untrusted files and archives safely and efficiently at volume
Broad understanding of web servers, frameworks, CDNs and proxies — how they reveal themselves through headers, cookies, page content, and certificates
Familiarity with software fingerprinting and vulnerability mapping — understanding how product versions map to known vulnerabilities (CPE, CVE)
Security
Working knowledge of cybersecurity principles and common security tooling
Demonstrated ability to apply secure coding practices and security-by-design thinking
Understanding of threat modeling, vulnerability patterns, and secure API design
Solid understanding of network security fundamentals — TCP/IP stack, DNS, TLS/SSL, HTTP/S, firewalls, and network traffic analysis
Familiarity with network attack patterns — DDoS, MITM, port scanning, packet sniffing, and lateral movement techniques
Experience working with or building systems that consume network telemetry data (e.g. NetFlow, PCAP, Zeek/Bro logs, SNMP)
Understanding of zero-trust architecture principles and how to apply them in distributed, internet-facing systems
Familiarity with security tooling such as Snort, Suricata, Wireshark, Nmap, or similar network inspection and monitoring tools
DevOps & Operational Awareness
Basic working knowledge of CI/CD pipelines — setting up, maintaining, and debugging pipelines (GitHub Actions, GitLab CI, Jenkins, or equivalent)
Comfortable with deployment workflows — deploying services to VMs and Kubernetes clusters, managing rollouts, and handling rollbacks
Hands-on experience debugging live services on Kubernetes — inspecting pods, reading pod logs, exec-ing into containers, and triaging crashloops or resource issues
Ability to fetch and analyse logs from VMs — SSH access, journald/systemd logs, log rotation, and using tools like grep, awk, tail, and jq to debug production issues
Familiarity with observability tooling — working with log aggregation (ELK, Loki) and basic metrics/alerting (Prometheus, Grafana) to diagnose issues in production
Understanding of Kubernetes autoscaling in depth — resource requests/limits, HPA, and event-driven autoscaling (KEDA); able to diagnose OOM kills, CPU throttling, and unclean shutdowns
Ownership & Leadership
5–8 years of backend engineering experience with a proven track record of delivering at scale
Self-driven and proactive — comfortable jumping into architectural decisions, raising concerns early, and owning outcomes without hand-holding
Proven ability to lead projects and mentor engineers
Strong written and verbal communication across technical and non-technical stakeholders
Nice to Have
Background in cybersecurity tooling, threat intelligence platforms, or security research
Exposure to cloud-native infrastructure (AWS, GCP, or Azure)
Experience with time-series databases (InfluxDB, TimescaleDB) relevant to security telemetry
Contributions to open-source AI or security tooling
Background in offensive security, penetration testing, or vulnerability research
Experience with threat intelligence work — tracking threat actors, leaked credentials, or underground forums
High-volume pattern matching or text processing — large regex sets or parsers working on untrusted, multilingual input at scale
Applied ML experience — clustering and visualising embeddings (e.g. UMAP) or other dimensionality reduction techniques for security data
Leading a project or initiative that spans multiple teams or services
- Department
- Engineering
- Location
- Dubai