Note: The job is a remote job and is open to candidates in USA. Smarsh helps organizations in regulated industries manage risk and gain intelligence from digital communications. The company is seeking a Senior Software Engineer to build and scale Python services and agentic AI systems for enterprise use, including LLM integrations, workflow orchestration, security, reliability, and real-time platform capabilities.

Responsibilities

• 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

Skills

• * 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

Company Overview

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