Description :

Role Overview

As AI Engineer Lead, you will be the architect and technical leader of the AI intelligence layer that powers the platform. This includes how LLMs, AI agents, machine learning workflows, and semantic reasoning are designed, integrated, governed, and scaled across the product.

This is a platform leadership role, not a research-only or experimentation role. You will design production-grade AI systems, data platform, and application workflows.

You will combine deep hands-on AI/ML expertise with system architecture thinking and team leadership, guiding engineers to build reliable, explainable, and extensible AI capabilities.

What You Will Own :

- AI platform architecture and long-term intelligence strategy

- LLM and agent-based system design and implementation

- Semantic-layer-driven reasoning (ontology, taxonomy, metadata)

- ML workflows from training to inference

- AI governance, reliability, and scalability

- Technical leadership and growth of the AI engineering team

Key Responsibilities :

AI Platform Architecture & Strategy :

- Define and own the AI architecture across the platform

- Design how AI systems interact with the semantic layer (ontology, taxonomy,relationships, metadata)

- Establish patterns for AI reasoning grounded in structured and semi-structured data

- Ensure AI components are modular, extensible, and reusable across product areas

- Act as the technical authority for AI-related design decisions

Semantic LayerDriven Intelligence :

- Design AI systems that leverage ontology- and taxonomy-based reasoning

- Define how entities, relationships, and domains inform AI responses and actions

- Guide implementation of semantic grounding for LLMs and agents

- Ensure AI outputs are explainable, consistent, and aligned with business semantics

- Partner with data and platform teams to evolve semantic models as AI needs grow

LLM, MCP & AI Agent Systems :

- Architect and implement LLM-powered workflows using modern orchestration patterns

- Design AI agent frameworks capable of planning, reasoning, and executing actions

- Apply MCP (Model Context Protocol or equivalent orchestration patterns) for context management

- Define strategies for prompt management, tool usage, memory, and agent coordination

- Balance flexibility with safety, performance, and cost controls

Machine Learning Workflows & Patterns :

- Design ML pipelines supporting feature engineering, training, evaluation, and inference

- Enable offline and online inference patterns where applicable

- Define versioning, reproducibility, and monitoring standards for models

- Partner with Data Engineering to ensure ML-ready datasets

- Support hybrid approaches combining classical ML with LLM-based intelligence

Platform Integration & Productionization :

- Ensure AI systems integrate cleanly with backend services and frontend workflows

- Design APIs and interfaces for AI capabilities across the product

- Own production concerns: latency, scalability, observability, and failure handling

- Ensure AI systems are secure, reliable, and enterprise-ready

- Guide teams on deploying AI features responsibly at scale

Team Leadership & Technical Guidance :

- Lead, mentor, and grow a team of AI and ML engineers

- Set technical standards for AI development and architecture

- Conduct design reviews and architecture walkthroughs

- Coach engineers to think in terms of systems, semantics, and platforms

- Foster a culture of rigor, experimentation, and ownership

Required Experience & Skills :

Core AI & ML Expertise :

- 810+ years in AI, ML, or applied intelligence engineering

- Strong hands-on experience with ML workflows and production systems

- Deep understanding of LLMs, prompt engineering, and agentic patterns

- Experience designing AI systems beyond simple chat interfaces

Semantic & Knowledge-Based Systems :

- Experience working with ontologies, taxonomies, or knowledge graphs

- Strong understanding of semantic modeling and structured reasoning

- Ability to design AI systems grounded in enterprise data models

- Experience aligning AI outputs with business semantics

Platform & Systems Thinking :

- Experience building AI platforms, not just point solutions

- Strong understanding of distributed systems and APIs

- Ability to balance long-term architecture with near-term delivery

- Experience designing reusable AI frameworks and services

Leadership & Collaboration :

- Proven experience leading or guiding technical teams

- Strong mentoring and coaching skills

- Ability to influence cross-functional architecture decisions

- Comfortable explaining AI systems to technical and non-technical stakeholders

- Product-minded approach to AI design

Why This Role Is Critical :

- You define how intelligence actually works in the platform

- Your designs determine trust, explainability, and actionability of AI outputs

- You bridge data, semantics, and AI into a coherent system

- You enable scalable AI capabilities across every business function

- You shape long-term differentiation as an intelligence platform, not just analytics

Ideal Candidate Profile :

This role is ideal for someone who :

- Has built AI systems in production, not just experiments

- Understands the importance of semantics and structure in AI

- Enjoys designing platforms and frameworks

- Can lead teams while remaining deeply technical

- Wants to shape the intelligence core of a growing AI company