Navsan
Website:
navsan.com
Job details:
Full-Time | Technology Leadership
Role Summary
Navsan is looking for an AI Technology Manager to lead the design and delivery of production-grade AI/ML systems. This role sits at the intersection of hands-on engineering and cross-functional leadership — you will architect LLM-powered pipelines, guide engineering teams, and translate complex AI capabilities into measurable business outcomes. You are equally at home reviewing a PR, presenting to stakeholders, and making pragmatic build-vs-buy decisions under uncertainty.
What You'll Do
- Architect end-to-end AI/ML pipelines: from data ingestion and model fine-tuning through to production serving, observability, and feedback loops.
- Lead design and delivery of LLM-powered applications — including document processing, multi-modal systems, and retrieval-augmented generation (RAG) workflows.
- Define and enforce engineering standards: SOLID principles, design patterns, async programming, structured logging, and custom exception hierarchies.
- Manage and mentor a team of ML engineers and Python backend developers; conduct code reviews, set technical direction, and grow engineering capability.
- Collaborate with product and business stakeholders to shape roadmaps, scope MVPs, and translate AI opportunities into delivery plans.
- Evaluate and integrate third-party AI services, cloud infrastructure (Azure, DigitalOcean), and tooling (Langchain, Langfuse, FastAPI, Streamlit).
- Drive DevOps and MLOps practices: CI/CD, pre-commit hooks, conventional commits, containerisation (Docker), and model lifecycle management.
- Own architecture decisions for distributed systems including task queues (Celery/Redis), API design, and monorepo structuring.
Required Skills & Experience
Technical Expertise
Leadership & Delivery
Python (advanced), FastAPI, Pydantic, async patterns
15+ years in software engineering, 5+ in ML/AI
LLMs: fine-tuning, RAG, prompt engineering, evaluation
Track record delivering AI products in regulated or enterprise contexts
ML frameworks: PyTorch, Hugging Face, vision-language models
Experience managing and mentoring engineering teams
Cloud platforms: Azure, DigitalOcean; Docker, CI/CD pipelines
Strong written and verbal communication with non-technical stakeholders
Data pipelines, Celery/Redis, structured logging, testing
Comfortable with ambiguity; able to drive decisions under uncertainty
Git best practices, monorepo patterns (uv workspaces), pre-commit
Consulting or client-facing experience is a plus
Nice to Have
- Background in capital markets, fintech, or other quantitative domains.
- Experience with vision-language model fine-tuning and image annotation pipelines.
Who Will Thrive Here
You are the kind of engineer who ships real things under real constraints — someone who knows that architectural judgment, not just coding ability, is what separates senior from junior work. You are opinionated about production quality but pragmatic about trade-offs. You view AI tooling as an accelerator on top of your own expertise, not a substitute for it.
Click on Apply to know more.