Top Talent HQ
Overview
Lead Backend Engineer – architect and own scalable infrastructure that powers emotion‑aware AI experiences. Work from the ground up, shape architecture decisions, and drive high‑impact innovation across the entire backend stack. Responsibilities
Backend Architecture & Infrastructure Leadership
Lead development and optimization of our core backend, initially built on C#/.NET/ASP.NET, transitioning toward Go or Python microservices. Architect and maintain secure, high‑performance REST and GraphQL APIs serving iOS app and internal tools. Design modular, scalable systems optimized for AI integration and rapid iteration. Build and deploy ML models via containerized microservices or serverless, incorporating async processing and pub/sub eventing. Manage CI/CD pipelines, observability, fault tolerance, and rollback strategies – owning the full backend lifecycle.
AI & Machine Learning Integration
Integrate and optimize inference with LLMs like OpenAI, Claude, and Llama, handling caching, routing, fallback logic, and embedding workflows. Maintain and evolve vector databases (e.g., PgVector) for memory and personalization. Develop internal tools and APIs for memory systems tied to user interaction, embeddings, and emotional context. Support emotion detection, sentiment analysis, and lightweight classifiers to power the emotional intelligence layer.
DevOps, Cloud Infrastructure & Data Engineering
Own and scale AWS infrastructure (ECS, Lambda, S3, RDS/Postgres, IAM, CloudFront, Route 53, etc.). Define architecture choices, optimize for latency, and ensure secure, SOC‑2‑ready operations. Use Terraform for infrastructure‑as‑code and manage CI/CD via GitHub Actions. Implement full‑stack observability with OpenTelemetry, Sentry, Grafana, CloudWatch, etc. Tune performance across the stack: index optimization, Redis/Valkey caching, streaming via pub/sub for real‑time responsiveness.
Personalization & Emotional Intelligence Systems
Build APIs and services that adapt dynamically to user behavior and emotional signals. Ship systems for custom user memory: embedding engines, experience graphs, and adaptive feedback loops. Collaborate cross‑functionally to fine‑tune the AI experience based on user sentiment, behavior, and evolving memory layers.
Tech Stack
Languages:
C#/.NET, Python, Go Frameworks:
ASP.NET, FastAPI, AWS Lambda Databases:
PostgreSQL, Redis/ValKey, PgVector, Neo4j/Neptune Cloud:
AWS (ECS, Lambda, S3, RDS Aurora, IAM, CloudFront, Route 53, SQS, SNS, SES) Infra & Tooling:
Docker, Terraform, GitHub Actions, OpenTelemetry, Sentry, Grafana Additional Information
We’re looking for builders, not just maintainers. Ideal candidates are highly motivated, proactive, and excited about contributing to a mission‑driven, deeply technical team. Startup‑ready mindset is a must – this is a hands‑on role with direct product impact. Job Details
Seniority level:
Mid‑Senior level Employment type:
Full‑time Job function:
Engineering and Information Technology Industries:
Business Consulting and Services
#J-18808-Ljbffr
Lead Backend Engineer – architect and own scalable infrastructure that powers emotion‑aware AI experiences. Work from the ground up, shape architecture decisions, and drive high‑impact innovation across the entire backend stack. Responsibilities
Backend Architecture & Infrastructure Leadership
Lead development and optimization of our core backend, initially built on C#/.NET/ASP.NET, transitioning toward Go or Python microservices. Architect and maintain secure, high‑performance REST and GraphQL APIs serving iOS app and internal tools. Design modular, scalable systems optimized for AI integration and rapid iteration. Build and deploy ML models via containerized microservices or serverless, incorporating async processing and pub/sub eventing. Manage CI/CD pipelines, observability, fault tolerance, and rollback strategies – owning the full backend lifecycle.
AI & Machine Learning Integration
Integrate and optimize inference with LLMs like OpenAI, Claude, and Llama, handling caching, routing, fallback logic, and embedding workflows. Maintain and evolve vector databases (e.g., PgVector) for memory and personalization. Develop internal tools and APIs for memory systems tied to user interaction, embeddings, and emotional context. Support emotion detection, sentiment analysis, and lightweight classifiers to power the emotional intelligence layer.
DevOps, Cloud Infrastructure & Data Engineering
Own and scale AWS infrastructure (ECS, Lambda, S3, RDS/Postgres, IAM, CloudFront, Route 53, etc.). Define architecture choices, optimize for latency, and ensure secure, SOC‑2‑ready operations. Use Terraform for infrastructure‑as‑code and manage CI/CD via GitHub Actions. Implement full‑stack observability with OpenTelemetry, Sentry, Grafana, CloudWatch, etc. Tune performance across the stack: index optimization, Redis/Valkey caching, streaming via pub/sub for real‑time responsiveness.
Personalization & Emotional Intelligence Systems
Build APIs and services that adapt dynamically to user behavior and emotional signals. Ship systems for custom user memory: embedding engines, experience graphs, and adaptive feedback loops. Collaborate cross‑functionally to fine‑tune the AI experience based on user sentiment, behavior, and evolving memory layers.
Tech Stack
Languages:
C#/.NET, Python, Go Frameworks:
ASP.NET, FastAPI, AWS Lambda Databases:
PostgreSQL, Redis/ValKey, PgVector, Neo4j/Neptune Cloud:
AWS (ECS, Lambda, S3, RDS Aurora, IAM, CloudFront, Route 53, SQS, SNS, SES) Infra & Tooling:
Docker, Terraform, GitHub Actions, OpenTelemetry, Sentry, Grafana Additional Information
We’re looking for builders, not just maintainers. Ideal candidates are highly motivated, proactive, and excited about contributing to a mission‑driven, deeply technical team. Startup‑ready mindset is a must – this is a hands‑on role with direct product impact. Job Details
Seniority level:
Mid‑Senior level Employment type:
Full‑time Job function:
Engineering and Information Technology Industries:
Business Consulting and Services
#J-18808-Ljbffr