Senior Product Manager, AI Products

Boostlingo

3h•Remote

About The Position

Boostlingo is seeking a Senior Product Manager to play a central role in its transformation into an AI-forward, enterprise-ready platform. This role is for a 'product builder' who understands deeply why they are building, prototypes solutions, uses AI to write and ship code, designs high-utility interfaces, pushes through blockers, and takes full-stack ownership of outcomes. The Senior Product Manager will lead the development of AI-native products from concept to production, identifying high-value AI opportunities, prototyping solutions, defining technical and UX direction, shipping product increments directly, partnering with engineering to harden and scale, and owning measurable business outcomes. This is not a coordination role, but a builder role where the individual is not just defining the roadmap but building it.

Requirements

5-7+ years in Product Management
Ideally with 2+ years building AI-driven products
Demonstrated hands-on prototyping experience
Strong technical fluency (former engineer or highly technical PM preferred)
Experience launching AI systems into production
Proven record of shipping 0→1 products
Experience with RAG architectures
Experience designing evaluation frameworks
Familiarity with cost optimization in AI systems
Exposure to AI safety and risk mitigation

Nice To Haves

Former engineer or highly technical PM preferred
Experience with RAG architectures
Experience designing evaluation frameworks
Familiarity with cost optimization in AI systems
Exposure to AI safety and risk mitigation

Responsibilities

Identify high-value AI opportunities
Prototype solutions using modern AI tooling
Define technical and UX direction
Ship product increments directly
Partner with engineering to harden and scale
Own measurable business outcomes
Identify real user problems worth solving with AI
Validate opportunity through direct user discovery
Build early prototypes using LLMs, APIs, scripts, and lightweight code
Design the interaction model and UX flows
Define system architecture in collaboration with engineering
Drive the product from 0 → 1 → scale
Operate across prompt design, RAG systems, evaluation frameworks, basic front-end and API experimentation, data instrumentation, and model iteration cycles
Define success metrics beyond surface engagement
Create evaluation loops (offline + online)
Design for uncertainty and guardrails
Balance accuracy, latency, and cost
Architect learning feedback loops
Partner closely with Design to shape problems before solutions
Co-create workflows that balance usability and feasibility
Pressure test concepts with engineering and research early
Use LLMs to write and debug code
Spin up lightweight services
Build internal tools and scripts independently
Prototype with Python / JavaScript or equivalent
Experiment directly with model APIs
Understand token economics and cost structures
Connect product initiatives to revenue or strategic advantage
Define clear success metrics
Prioritize ruthlessly
Cut scope intelligently
Kill weak ideas early
Push through ambiguity
Solve blockers independently
Figure things out
Prefer action over alignment theater
Escalate only when truly necessary
Understand how data, models, UX, and economics interconnect
Anticipate scaling issues early
Think in feedback loops
Design with monitoring and evaluation in mind
Understand LLM behavior, embeddings, RAG, fine-tuning
Know the difference between deterministic and probabilistic systems
Design around hallucination risk
Have hands-on experience building with AI APIs
Are already using AI daily to accelerate your workflow
Built side projects using AI
Shipped prototypes independently
Written production or near-production code
Created technical demos without waiting on engineering
Experimented with model tuning or evaluation pipelines
Can clearly articulate why something should exist
Distinguish novelty from utility
Prioritize based on leverage
Understand user psychology and workflow design
Break large problems into shippable increments
Avoid gold-plating
Ship early versions quickly
Iterate based on real usage
Measure outcomes rigorously
Drive cross functional outcomes across teams
Shipped at least one AI product with measurable user impact
Built a sustainable feedback loop for model improvement
Demonstrated cost-aware scaling
Established strong product-engineering trust
Elevated the organization’s standard for AI product quality