Smarsh
via Lever
Senior Software Engineer, Python + AI Platform
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 1d ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 28d ago
The date the source published, not the day we noticed it (2026-08-17). Last seen at its source just now.
Is it remote?
US - Remote
That is the location the employer filed this posting under. Quoted as written — we do not re-word the source's own location.
Who may apply?
United States
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay $161k–210k/yr
Middle 50% of 741 listings that do state pay — Engineering · Senior · United States · USD/year. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
-
lever employer's own board first seen 28d ago · last seen just now
The listing
Smarsh is hiring a senior backend/platform engineer to build and scale agentic AI systems for enterprise use. You will build fast-moving, early-stage Python services that integrate AI capabilities into a production agentic platform. Your scope spans workflow execution, scale, reliability, and platform hardening as we grow.
This is not a generic backend role. The focus is building and designing agentic systems, shipping working software, and solving hard platform problems in a fast-moving AI-native environment. You will join a small, high-velocity cross-functional group and own problems end to end, designing and building from scratch, making fast architectural calls, and driving ideas from whiteboard to working system with a small, high-agency team.
What will you do?
- Drive backend development for AI workflows as part of a collaborative team. Build and evolve Python/FastAPI services powering core agentic workflows and platform capabilities.
- Productionize LLM integrations. Implement systems around Bedrock usage, quotas, retries, failover, cost controls, model configuration, and approval constraints.
- Design for security and compliance. Address customer data handling, tenant isolation, auditability, observability, and secure processing for regulated workloads. Apply auditable data design patterns to ensure AI outputs are traceable, reproducible, and built to withstand regulatory scrutiny.
- Build for scale. We're a nimble team, but our enterprise customers process data at petabyte scale. Help the platform grow to meet that bar through async job orchestration, performance tuning, and data-layer optimization.
- Support multi-tenant architecture. Contribute to tenant-aware services, role-based access, SSO integration, and admin/reporting capabilities.
- Improve platform reliability. Add monitoring, tracing, alerting, and operational tooling for LLM pipelines, workflow execution, and report generation.
- Build real-time capabilities. Design and implement real-time event delivery and pub/sub patterns to support live workflow state, notifications, and agent feedback loops.
- Contribute to technical decisions. Partner on shared services decisions, platform architecture, and integration boundaries across the stack.
- Work across ambiguity. Translate evolving product requirements and non-functional requirements into practical technical solutions with product, architecture, legal, and security stakeholders.
- Champion code quality. Drive strong typing, automated testing, and continuous integration practices that keep the team fast and safe.
- Design typed API contracts. Own the API surface as a product contract: designing clean, schema-driven APIs that support typed client generation and reliable integration across services.
What will you bring?
- Strong Python backend engineering. 7+ years professional software development, including 5+ years building Python services in production. Deep experience with APIs, async processing, background jobs, and workflow orchestration.
- Cloud-native backend experience. AWS experience, ideally with services relevant to secure enterprise workloads (compute, storage, networking, CI/CD, identity, secrets, encryption).
- Production distributed systems. Proven ability to productionize complex backend systems with reliability, observability, retries, throughput, failure handling, and performance tuning.
- Data-intensive system design. Strong knowledge of PostgreSQL, large-scale data processing patterns, indexing, query tuning, and batch/stream tradeoffs. Experience with retrieval-augmented generation (RAG), vector search, and embedding-based systems is required (not a plus).
- Security and compliance mindset. Experience with multi-tenant systems, RBAC, audit logging, secure data handling, and regulated environments.
- Strong ambiguity handling. Ability to work from partial requirements and shape implementation around product and non-functional requirement constraints.
- Agentic workflow engineering. Hands-on experience building LLM-driven workflows: tool-calling, state machines, human-in-the-loop approval patterns, checkpoint/resume, and multi-step agent orchestration. Familiarity with frameworks like LangGraph or equivalent.
- AI-native engineering. Experience working on or alongside AI-native engineering teams, where AI agents are first-class participants in the development workflow, not just productivity tools. Includes hands-on prompt engineering, eval design, and LLM cost optimization: caching strategies, token efficiency, and model selection tradeoffs.
- Product mindset. Bias for shipping, learning from real usage, and making pragmatic tradeoffs grounded in customer problems.
- LLM / AI platform experience. Bedrock, OpenAI, Anthropic, LangChain/LangGraph, prompt workflows, evals, tool-calling systems. Experience integrating external AI services safely and reliably.
- Identity and access. SSO/SAML/OIDC, enterprise auth patterns.
- Graph-shaped data and entity resolution. Experience with graph-backed data models, entity deduplication, mention linking, and building systems that reason over connected, structured records.
- Observability stack. OpenTelemetry, tracing, metrics, alerting, cost/usage dashboards.
- Regulated communications or compliance domain. Background in systems that handle sensitive communications, audit trails, or data subject to legal or regulatory review is a meaningful differentiator.
- Infrastructure as code. Terraform, feature flags, canary deployments, release strategies.