Tech Lead (AI + Full Stack) at InVitro Capital building AI-native products with hands-on engineering and leadership.
Responsibilities
Architect core systems
Build them with your own hands
Integrate AI tooling & agents
Mentor engineers through technical leadership
Drive engineering excellence across multiple ventures
Build & Lead With Technical Depth (70–90% hands-on)
Architect, build, and ship backend and full-stack systems end to end.
Lead engineers through code reviews, architecture reviews, and solid technical decision-making.
Own engineering execution across multiple ventures.
Design Scalable, AI-Native Architectures
Build modular APIs, distributed systems.
Embed AI capabilities into product systems (LLMs and/or domain AI like NLP/CV) in a way that is secure, observable, reliable, and scalable.
Design and implement retrieval workflows (RAG), evaluation/guardrails, and agent/tool orchestration where they add real product value.
Work with Python, OpenAI, Anthropic, LangGraph, LangChain, LlamaIndex, vector databases, and agent toolchains.
Engineer With Excellence
Write high-performance, production-grade code in Python (primary) for backend services and AI/ML systems.
Build and productionize ML/AI components (training/inference pipelines, model serving, and integrations) with strong engineering discipline.
Build systems optimized for reliability, performance, and observability.
Full-Stack Ownership
Collaborate with frontend engineers to deliver seamless end-to-end experiences.
Design clean APIs, interfaces, and developer workflows.
DevOps + Cloud Execution
Manage CI/CD pipelines and cloud deployments on AWS.
Kubernetes is a strong plus; AWS + Docker + CI/CD and production deployment ownership are required.
Ensure systems are scalable, fault-tolerant, secure, and well-instrumented.
Own production deployments of AI/ML components (model serving, monitoring, and lifecycle workflows) alongside the core application stack.
Technical Leadership & Mentorship
Mentor engineers and uplift technical standards across the stack.
Provide architectural direction and guide complex technical initiatives.
Contribute to hiring and shaping engineering culture.
Requirements
Professional Engineering Experience — 12+ years building and shipping production-grade systems.
Python Expertise (FastAPI or similar, production) — async services, Pydantic models, you write Python daily.
Backend/System Architecture Ownership — you’ve owned system design decisions, led code reviews, mentored engineers, and shipped end-to-end systems as a senior IC or tech lead.
Core ML / Deep Learning (hands-on) — built ML/DL systems using PyTorch or TensorFlow; understand training, evaluation, optimization.
Production ML Deployment / MLOps — deployed ML systems to production; owned monitoring, versioning, rollback strategies, lifecycle management.
Applied AI Systems Experience — shipped AI-powered features using LLMs and/or applied NLP/CV in real products (not experimentation only).
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