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5+ years of hands‑on software engineering experience building production systems at scale.
Strong proficiency in Python and solid experience with React..
Deep experience with cloud infrastructure (AWS), containers (Docker, Kubernetes), and distributed systems.
Professional experience designing event‑driven or microservice architectures (e.g., Kafka/PubSub, queues, webhooks).
Practical knowledge in data stores (Postgres, Redis, or equivalent) and analytics (SQL, Snowflake/BigQuery).
Strong grounding of LLM/AI application patterns (RAG, tool use, function calling, guardrails) and vendor APIs (OpenAI or similar).
Experience with vector search (pgvector, Pinecone, OpenSearch), feature/semantic layers, or retrieval pipelines
Familiarity with LLMOps: eval frameworks, prompt/version management, offline/online A/B testing, and cost/latency optimization
Excellence in testing (unit/integration), observability, and CI/CD; you instrument before you ship.
Clear written and verbal communication; able to drive alignment with concise design docs and reviews.
Build core AI platform services - Design and implement agent orchestration, prompt management, RAG, Connectors, and evaluation pipelines that power AI experiences across the company.
Develop complex agentic workflows - Develop a multi-step workflow that coordinates tools and services with proper observability, guardrails, and cost controls (using OpenAI Agent SDK, LangGraph, or a similar framework).
Build LLM evaluation and optimization systems -Develop evaluation harnesses, offline/online experiments, prompt-testing frameworks, and dashboards to balance quality, latency, and spend across all AI services.
Ship the internal AI portal - Build user-facing products that democratize AI access: prompt directories, agent designers, templates, approval workflows, and more.
Ensure security and compliance - Develop RBAC, data classification, PII redaction, audit logging, and policy enforcement.
Create robust integrations - Connect the platform with Salesforce, NetSuite, Slack, Snowflake, HRIS, and other critical business systems.
Establish operational excellence - Implement CI/CD, testing strategies, monitoring/tracing, and SLOs for high-reliability services.
Enable developers - build SDKs, CLIs, and templates that help teams create safe, scalable AI workflows. Lead technical discussions and write design documentation.
Experience building developer platforms or internal tooling
Familiarity with workflow orchestration (Airflow, Prefect, Dagster) or multi-model routing strategies
Hands-on experience with model optimization, fine-tuning, or distillation techniques
Background in SaaS/enterprise environments with compliance requirements (SOC2, GDPR)