Designation: AI/ML Engineer
We are seeking an experienced AI/ML Engineer with a strong Product Mindset who treats code as a means to a strategic end. In this role, you will serve as the architect of our product's intelligence, building the mathematical logic and predictive models that directly map our product path.
This position is specifically designed for an impact-driven builder looking to evolve into strategic leadership. You will completely own the analytical engine of the enterprise today, establishing a clear, defined pathway toward owning the overall Product Roadmap tomorrow.
CORE MISSION & KEY RESPONSIBILITIES
1. Project Ownership & Strategic Impact
Manage Multi-Project Delivery: Take end-to-end ownership of managing 2+ concurrent ML projects, ensuring high-quality execution and delivery excellence from discovery to deployment.
Define Action Plans: Translate complex business demands into highly structured, actionable milestones and modeling deliverables with clearly predictable business outcomes.
Architect the Logic: Design, train, validate, and refine core machine learning models (including Classification, Regression, and Time-Series Forecasting) to solve critical operational bottlenecks.
Rapid Prototyping: Construct robust, functional proofs-of-concept using Python, delivering validated algorithmic logic and mathematical frameworks ready for the engineering team to scale.
Algorithmic Strategy: Own the strategic trade-offs between computational complexity and immediate business value. Decide whether a challenge warrants a rules-based engine, classic regression, or an advanced deep learning framework.
2. Advanced Technical & Data Strategy
Signal Discovery: Move beyond existing data parameters; actively dictate the data the platform needs by defining upstream feature requirements and event tracking infrastructure to capture high-fidelity user signals.
Cross-Module Data Integration: Seamlessly integrate complex data streams across disparate product modules and establish continuous tracking systems for data health indicators.
Metric Definition: Eliminate dependency on vanity metrics. Define and establish product "North Star" KPIs (e.g., Customer Lifetime Value, Retention, Engagement Quality) that strictly align algorithm performance with organizational health.
3. Product Experimentation & Visibility
Hypothesis Generation: Mine deep data landscapes to systematically identify user friction points, formulating data-backed features and iterations to eliminate them.
Statistical Rigor: Design comprehensive frameworks for A/B testing and product experimentation, establishing mathematical parameters that distinguish true actionable trends from statistical noise.
Lead High-Impact Initiatives: Cultivate internal visibility and present compelling data narratives to senior leadership that directly shape the active pipeline of what we build next.
THE IDEAL CANDIDATE PROFILE
Experience & Background
3 to 6 years of professional experience serving as an AI/ML Engineer or Data Scientist, preferably within an IT, software, or B2B SaaS environment.
Demonstrated track record of successfully extracting models from isolated research environments and scaling them into live, production-grade enterprise software.
Proven capability in scoping multi-layered projects and independently driving measurable, numbers- backed business outcomes.
The Technical Toolkit
Python Proficiency & Modular Coding: A non-negotiable baseline. Exceptionally fluent in Python with strict adherence to enterprise-grade modular coding standards. Ability to write highly organized, clean, object-oriented, testable, and reusable code built for production environments—standalone Jupyternotebooks are strictly treated as draft spaces.
Advanced SQL & Data Manipulation: Advanced command of complex SQL syntax and absolute mastery over the core Data Science stack (Pandas, NumPy, Scikit-Learn) to seamlessly manipulate and derive value from massive datasets.
Strong Statistical Foundation: A deep mathematical grasp of probability distributions, rigorous hypothesis testing, and systematic error analysis. You must understand the underlying mechanics of why an algorithm behaves the way it does.
Applied AI Engineering: Direct, hands-on experience building with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) architectures, and autonomous agentic workflows. Proven capability to engineer context, fine-tune models, and develop production loops.
Production Deployment & MLOps: Practical experience bridging the gap between an MVP and a hardened enterprise ecosystem. Comfortable working with automated pipelines, API integrations, and robust infrastructure parameters to ensure model stability.
Data Visualization: Competency in constructing insightful data visualizations (Matplotlib, Seaborn) that clarify complex multi-dimensional variations for executive decision-makers.
Strategic Leadership & Product Mindset
Outcome Over Output: Strongly prioritize moving core business metrics over marginally optimizing model hyperparameters. A simple model that successfully ships to solve a client pain point is valued infinitely more than a complex framework that remains grounded.
Business & Domain Mastery: Possess strong situational awareness regarding core customer pain points
and shifting competitor trends within the technology landscape.
Collaboration & Mentorship: Actively mentor junior technical specialists, share technical methodologies, and foster an engineering culture centered around clean product-first thinking.
Executive Communication: Masterfully align professional priorities. Ability to articulate the high-level "Business Value" of a complex model to executive leadership, and seamlessly transition to mapping the exact "Mathematical Logic" for software engineers.