DeepIntent is the leading healthcare marketing platform, purpose-built to help marketers plan, activate, and optimize data-driven campaigns with speed and precision. Trusted by the world’s top healthcare brands and their agencies, DeepIntent uniquely unites media, identity, and real-world clinical data to power privacy-safe, omnichannel marketing across every screen. Backed by patented technology and proven outcomes, DeepIntent’s platform delivers measurable audience quality and script lift at scale. Learn more at www.deepintent.com.

What You'll Do:

Provide technical leadership across multiple teams and backend systems, owning the architecture of high-throughput, low-latency services built primarily on Java and Spring Boot.
Drive architecture decisions for distributed systems, including service boundaries, data modelling, storage choices, and scalability, so the platform stays future-proof as volume and complexity grow.
Tackle the most complex architectural challenges in your area, and partner with Tech Leads and Engineering Managers to shape the technical vision and roadmap.
Lead critical cross-team technical initiatives end to end, from design through rollout, aligning engineering work with product and business goals.
Act as the technical lead for your product line, breaking down complex problems into well-defined work, providing clear technical guidance, and unblocking engineers.
Mentor senior and junior engineers through design reviews, code reviews, and pair programming, raising the technical bar and fostering a culture of ownership and continuous learning.
Design and build systems that make effective use of Redis across a range of patterns, such as caching, real-time counters, rate limiting, distributed locking, pub/sub, and streams, alongside other datastores.
Build and evolve event-driven and asynchronous systems using messaging and streaming platforms such as Kafka.
Lead the integration of AI into our products and engineering workflows, including LLM-powered features, agentic workflows, retrieval-augmented generation (RAG), and tool integrations via the Model Context Protocol (MCP).
Champion AI-assisted software development across the team, setting standards for how engineers use coding agents and AI tools responsibly to improve speed and quality.
Establish and enforce engineering best practices around API design, code quality, testing, security, and data privacy, with healthcare compliance expectations in mind.
Design observability strategies (metrics, logging, tracing, alerting) using tools such as Grafana and Prometheus to proactively identify bottlenecks and ensure production stability.
Lead deep-dive investigations into complex production issues and drive root-cause fixes and blameless postmortems.
Drive knowledge sharing across engineering through technical documentation, design forums, and internal talks.

Who You Are

8+ years of backend engineering experience, with a track record of designing and delivering large-scale distributed systems in production.
Deep expertise in Java (17+) and the Spring ecosystem (Spring Boot, Spring Cloud, Spring Data), including a solid understanding of JVM internals, concurrency, and performance tuning.
Strong working knowledge of Redis, including its data structures, persistence and clustering models, and how to apply it to problems beyond caching, with a clear understanding of its trade-offs at scale.
Solid experience with relational and NoSQL databases (e.g., MySQL, PostgreSQL, Aerospike, Cassandra or similar), including query optimisation and schema design.
Experience with messaging and streaming systems such as Kafka, and with designing resilient, idempotent, event-driven architectures.
Strong grounding in microservices, REST/gRPC API design, and distributed systems fundamentals (consistency, partitioning, fault tolerance).
Experience with cloud platforms (GCP or AWS), containerisation (Docker, Kubernetes), and owning CI/CD pipelines.
Working knowledge of current AI trends and their practical engineering application: LLM APIs, prompt and context engineering, agentic systems, RAG and vector databases, MCP, and evaluation of AI system quality and reliability.