AI Engineering Lead
Responsibilities:
Technical Strategy & Architecture
- Develop a scalable and modular AI architecture that integrates seamlessly with existing customer platforms;
- Define and implement best practices for AI model deployment, data processing, and cloud infrastructure;
- Establish a clear roadmap for AI-driven capabilities, ensuring alignment with business goals.
AI & Data Engineering
- Design and implement Retrieval-Augmented Generation (RAG) pipelines and AI-driven data processing workflows;
- Design and develop complex agent-based solutions;
- Develop and optimize AI models tailored to enhance automation, personalization, and insights for client users;
- Ensure the integration of structured and unstructured data sources into AI workflows;
- Establish data governance frameworks, security models, and compliance strategies for AI usage.
Solution Development & Implementation Planning
- Collaborate with UX and product teams to define AI-powered user experience improvements;
- Identify and implement technical accelerators to optimize development speed and platform efficiency;
- Define technical milestones, effort estimations, and cost evaluations for implementation.
Collaboration & Stakeholder Management
- Work closely with product managers, designers, and engineers to align technical solutions with business needs;
- Provide technical leadership to engineering teams, guiding them in AI model deployment and system scalability;
- Engage with stakeholders to validate architectural decisions and refine technical requirements.
Performance Optimization & Scalability
- Establish monitoring frameworks for AI model performance, system reliability, and infrastructure scalability;
- Optimize AI pipelines and data processing layers to ensure real-time insights and efficient workflows;
- Address system bottlenecks and propose enhancements to improve user experience and cross-platform integrations.
Technical Expertise
- Proven experience as an AI Tech Lead or ML Engineer, ideally in customer-facing, production-deployed projects;
- Solid understanding of deep learning concepts, supervised / unsupervised / self-supervised / reinforcement learning;
- Solid understanding of Large Language Models, Transformers architecture, self-attention, mixture of experts, and embedding models;
- Proven experience with advanced Retrieval Augmented Generation, vector DBs, and prompt engineering;
- Expertise with AI agents design, orchestration and optimisation;
- Experience with CrewAI, LangChain / LangSmith / LangGraph, and/or LlamaIndex;
- Experience with model fine-tuning;
- Hands-on data pre-processing experience;
- Strong Python expertise;
- Proficiency in ML frameworks such as PyTorch, TensorFlow, or similar;
- Experience with AWS development and deployment (ECS, Lambda, S3);
- Experience with at least one of the following cloud-based AI platforms (preferrably AWS): AWS SageMaker / AWS Bedrock / Azure ML;
- LLMOps expertise;
- Familiarity with Docker and Kubernetes.
Leadership & Communication
- Strong ability to translate technical concepts into business-impact discussions;
- Experience leading AI engineering teams and working in cross-functional environments;
- Track record of working with product and business teams to define AI-driven solutions.
Strategic & Problem-Solving Skills
- Ability to assess existing systems, identify gaps, and develop AI-driven enhancements;
- Experience in defining and implementing AI strategies for enterprise-grade products;
- Strong analytical mindset with a focus on performance optimization and data-driven decision-making.
