Machine Learning Engineer

As a Machine Learning Engineer, AI & ML — Data Collection, you will contribute to building and scaling the company’s Unified AI/ML Data Collection Platform, enabling standardized, reliable, and scalable machine learning capabilities across the organization. This role will focus on developing and supporting AI/ML and LLM-driven data systems that power data pipelines, model lifecycle management, evaluation frameworks, and production deployment.

This position requires hands-on experience in machine learning engineering, LLM-based systems, ML platform development, and MLOps. You will work closely with ML engineers, product managers, researchers, and business stakeholders to deliver production-ready AI/ML capabilities aligned with broader business objectives and AI/ML strategy.

You will be involved in the design, development, testing, deployment, and support of platform components, including data ingestion, feature management, model training and evaluation, scalable inference systems, and model observability capabilities.

You will help build AI/ML systems that are production-ready, observable, maintainable, and cost-efficient, with an emphasis on reliability, performance, governance, and developer productivity. You will work with technologies and patterns related to large language models (LLM), retrieval-augmented generation (RAG), embeddings, vector databases, distributed systems, cloud-native architectures, and ML Operations (MLOps).

You will contribute to the end-to-end lifecycle of ML systems, from experimentation and prototyping to deployment, monitoring, optimization, and continuous improvement, while working with peers and contributing to strong engineering practices across the team.

Team Overview

You will be part of a multidisciplinary team of ML engineers responsible for building and maintaining the Unified AI/ML Data Collection Platform. The team focuses on developing scalable systems that support data pipelines, model lifecycle management, LLM-based workflows, and evaluation frameworks, enabling downstream teams to build and deploy AI-driven data collection solutions.

Outline of Duties and Responsibilities

• AI-Powered Data Collection Systems: Develop and support scalable AI-driven data collection and enrichment workflows across structured and unstructured data sources.

• LLM & Generative AI Workflows: Build and maintain LLM-based capabilities including RAG systems, prompt orchestration, entity extraction, summarization, classification, and automated validation workflows.