Smarsh
Website:
smarsh.com
Job details:
Smarsh is the leader in communications compliance, archiving, and analytics. We provide compliance across the broadest set of communications channels with insights on what’s being captured. Smarsh customers manage over 500 million daily conversations across 80 channels and growing. Customers include the top 10 U.S., top 8 European, top 5 Canadian, and top 3 Asian banks. The Smarsh advantage is customers stay ahead of compliance and uncover patterns and relationships hidden within their data.
At Smarsh, we’ve been helping our customers manage new forms of communication since 1998. We work closely with regulators including the SEC, FINRA, IIROC, and the PRA and FCA, and with our customers, to ensure that they understand the capabilities of today’s technology and that our platform meets their most stringent requirements. Our products include Connected Capture, Connected Archive, Web Archive & Business Solutions.
About the team
Cognition Logic makes AI/ML usable, scalable, and consumable across the Smarsh product estate. The team bridges core Cognition capabilities with Conduct, eDiscovery, and other parts of the Smarsh product portfolio through three modes of operation: Forward Deployed Engineering (embedding within product teams to deliver AI/ML capability end-to-end); integration and pipeline engineering (owning the high-throughput data paths that move content reliably through Cognition services); and domain boundary enforcement (identifying product-specific behaviour that has accumulated inside shared AI services over time and returning it to the product teams that own it — keeping Cognition focused and the domain model honest).
What You Will Do
Forward Deployed Engineering
- Embed within product teams to deliver end-to-end AI/ML integrations — API orchestration, workflow management, and error handling — directly inside the host team's codebase and release processes.
- Produce structured handover artefacts for every engagement: merged PRs, architecture notes, runbooks, observability dashboards, and knowledge transfer sessions.
Integration & Pipeline Engineering
- Implement and extend Cognition's integration patterns — coordinator/extractor/mailbox services, Kafka pipeline patterns, and RESTful API contracts.
- Own and scale the Cognition v3 architecture — data pre-processing, post-processing, the audit sub-system, and the Conduct integration pipeline — handling high message volumes with reliable throughput.
- Instrument services with Prometheus and Grafana; maintain observability across Logic's owned components.
AI Enablement & Engineering Standards
- Champion and actively use AI productivity tooling (Windsurf, Claude Code) and embed these practices in the teams you work with.
- Write clean, well-tested Kotlin (or Java); contribute to architecture and design reviews across Logic and host teams.
What You Bring
Experience & Ways of Working
- 6+ years building and operating backend services in a JVM language — Kotlin preferred, strong Java equally welcome.
- Proven track record delivering production-grade integrations across messaging systems, RESTful APIs, and cloud-native infrastructure.
- Comfortable working across unfamiliar codebases and teams — you pick up conventions quickly and contribute confidently.
- Strong engineering judgment: evaluates trade-offs, adapts approaches, and works independently with minimal supervision; work reviewed at critical decision points.
- Clear communicator with senior stakeholders unfamiliar with the subject matter; adapts style to audience and produces thorough handover documentation.
Technical Skills
- Kotlin (or Java with willingness to learn Kotlin) — service development, idiomatic patterns, production readiness.
- Apache Kafka — producing, consuming, stream processing, and operational patterns at scale.
- Kubernetes (EKS or equivalent) — workload deployment, config management, debugging.
- RESTful API design and implementation; AWS S3 and broader AWS ecosystem.
- Awareness of AI/ML architectures and common patterns — RAG, prompt engineering, LLM-based capability development — or willingness to build that context on the job.
- Familiarity with AWS AI/ML services such as Amazon Bedrock or SageMaker; production experience is a plus, not a prerequisite.
- Observability: Prometheus metrics instrumentation and Grafana dashboards.
- Active user of AI-powered coding assistants (Windsurf, Claude Code, or similar).
Why Smarsh?
Smarsh hires lifelong learners with a passion for innovating with purpose, humility and humor. Collaboration is at the heart of everything we do. We work closely with the most popular communications platforms and the world’s leading cloud infrastructure platforms. We use the latest in AI/ML technology to help our customers break new ground at scale. We are a global organization that values diversity, and we believe that providing opportunities for everyone to be their authentic self is key to our success. Smarsh leadership, culture, and commitment to developing our people have all garnered Comparably.com Best Places to Work Awards. Come join us and find out what the best work of your career looks like
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