AI Engineer

Introduction

We are looking for an AI Engineer to join our Analytics practice, where you will design and build applied AI solutions. This role combines strong Python skills with hands-on experience across the AWS stack, LLMs, RAG, and AI agents — paired with the ownership to manage client conversations and see deliverables through end to end.

Responsibilities

• Design, build, and deploy AI/ML solutions in Python, covering everything from data pipelines to model serving.
• Build and maintain Retrieval-Augmented Generation (RAG) systems and LLM-based applications for real business use cases.
• Develop and deploy AI agents that automate multi-step workflows and decision-making.
• Work with the AWS stack (e.g. SageMaker, Lambda, Bedrock, S3) to build and scale AI/ML infrastructure.
• Apply core ML concepts — evaluation, fine-tuning, embeddings, vector search — to improve solution quality.
• Own client-facing deliverables — from requirement discussions through to final delivery — ensuring solutions meet business needs.
• Collaborate with cross-functional teams — data engineering, product, and delivery — to integrate AI solutions into broader systems.
• Proactively identify opportunities to improve existing AI/ML solutions and ways of working.
• Document architecture, decisions, and learnings to support team-wide knowledge sharing.

Requirements

• 4+ years of experience building AI/ML or software solutions, with hands-on delivery experience.
• Strong proficiency in Python for building and deploying AI/ML applications.
• Working experience with the AWS stack for deploying and scaling AI/ML solutions.
• Practical experience building with LLMs, including prompt design, fine-tuning, or evaluation.
• Hands-on experience building RAG (Retrieval-Augmented Generation) pipelines.
• Experience designing or deploying AI agents for task automation.
• Solid understanding of core ML concepts — model evaluation, embeddings, vector search, and related techniques.
• Experience managing client relationships and owning deliverables end-to-end.

Core Skills

• Technical Skills — Model development, prompt engineering, RAG pipeline design, agent orchestration, ML evaluation.
• Tools & Technologies — Python, AWS (SageMaker, Lambda, Bedrock, S3), RAG frameworks, vector databases, LLM frameworks (e.g. LangChain, LlamaIndex)
• Soft Skills — Accountability, proactive ownership, client management, clear communication.

Preferred / Good to Have

• Experience in the Retail industry or retail-adjacent use cases.
• Exposure to MLOps practices for deploying and monitoring AI/ML systems in production.
• Experience with containerization (Docker) and orchestration (Kubernetes) for AI workloads.
• Relevant certifications in AWS, AI/ML, or cloud platforms.

Quality Compliance

Compliance to Quality and Information Security is critical in ensuring the integrity, confidentiality, availability of data and the consistent delivery of high-quality services are an important aspect of hiring for this position.