AI Platform Lead (Hands-On)

Role Overview

We are looking for a hands-on Tech Lead to play a leading technical role in the development of AI-powered, agentic safety and decision platform.

This is a senior, highly hands-on role for a strong engineer who enjoys building real systems, writing production-grade code, and leading complex technical initiatives. You will be a key technical figure in the team, working closely with the product, business, architect, and other development teams stakeholders.

You are expected to deeply understand it, influence architectural decisions, and ensure high-quality implementation aligned with the architectural vision.

The role combines deep technical execution with strong product, ML, and business understanding. You will help shape how AI-driven safety capabilities are implemented, delivered, and scaled.

Key Responsibilities

Technical Leadership

• Act as a leading technical figure within the team, working in close collaboration all other stackholders
• Deeply understand the architecture and influence architectural decisions through design, feedback, and execution.
• Lead the technical implementation of AI-driven and agentic workflows.
• Stay deeply hands-on with code, design discussions, and critical problem-solving.
• Drive engineering best practices around scalability, reliability, security, and performance.
• Review code and designs, set high engineering standards, and continuously raise the technical bar.
• Manage the day-to-day team activities, backlog and tasks

Hands-On Development

• Write high-quality production code across backend and AI services, using languages such as Python and other modern backend languages.
• Build and maintain backend services, APIs, and data pipelines.
• Implement ML and LLM-powered services and inference pipelines in production environments.
• Design and implement agentic workflows, asynchronous processing, and event-driven systems.
• Troubleshoot complex production issues and lead root-cause analysis.

Product & Business Alignment

• Work closely with Product Management to translate business and safety requirements into technical solutions.
• Understand customer workflows, operational pain points, and business objectives.
• Make pragmatic technical decisions that balance speed, quality, and long-term scalability.
• Help shape the product roadmap from a technical feasibility and architecture perspective.

Delivery & Execution

• Take ownership of project planning, execution, and delivery for initiatives.
• Break down complex initiatives into clear, executable technical plans.
• Ensure predictable delivery while managing technical risks and dependencies.
• Collaborate closely with DevOps, Data, and other engineering teams.

Collaboration & Mentorship

• Act as a technical mentor and go-to expert for engineers
• Foster a culture of ownership, accountability, and continuous improvement.
• Provide technical guidance without heavy process or bureaucracy.

What We’re Looking For

Requirements:

• 7+ years of hands-on software engineering experience, including a senior or tech lead role.
• Proven experience building, deploying, and operating ML and LLM-based systems in production.
• Hands-on experience designing and implementing AI agents, orchestration layers, or decisioning platforms.
• Strong backend engineering background using modern languages such as Python, Node, C#, or similar.
• Practical experience with agent frameworks and orchestration tools such as LangGraph, LangChain, or equivalent.
• Solid understanding of distributed systems, APIs, and event-driven architectures.
• Experience integrating LLMs, tools, and external data sources into real-world systems.
• Experience with PostgreSQL and large-scale data infrastructure, including Kafka, RabbitMQ, or similar technologies.
• Experience working in cloud environments, preferably AWS, and with containerized systems using Docker and Kubernetes.
• Strong product and business understanding, with the ability to translate customer needs into technical solutions.
• Excellent communication skills, including customer-facing communication and technical representation.
• Fluent, professional English, written and spoken.
• Strong sense of ownership and accountability, and a true hands-on leader who leads by example through code and execution.
• Experience with additional AI technologies such as vector databases, embeddings, retrieval-augmented generation (RAG), prompt engineering, model evaluation, and AI observability.
• Familiarity with real-time or streaming AI systems and production monitoring of ML/LLM behavior.