Machine Learning Architect

Role Overview

Join a high-impact Data & AI team building scalable machine learning solutions and Azure-based data platforms. You will develop production-grade ML workloads, build LLM applications, and help enterprise customers leverage AI to drive business growth and efficiency.

Why This Role Matters

We are redefining how organizations use Data and AI to simplify complex workflows, accelerate innovation, and achieve scalable business outcomes. This role focuses on translating machine learning and Generative AI capabilities into reliable, production-ready solutions.

Key Responsibilities

• Build and scale customer-facing data science and machine learning workloads.
• Implement MLOps practices to deploy, monitor, and maintain production ML models.
• Develop LLM-based solutions, including RAG architectures over enterprise knowledge repositories.
• Build natural-language interfaces for querying structured data and develop AI-powered content generation solutions.
• Advise customer data teams on ML architecture, tools, deployment strategies, and best practices.
• Build scalable ML pipelines and implement model performance and drift monitoring.
• Collaborate with customer stakeholders to translate business requirements into technical solutions.
• Work with distributed data processing frameworks to handle large-scale datasets.Core Technical Requirements
• Hands-on experience with machine learning, data science, and production ML systems.
• Strong Python skills and experience with libraries such as pandas, scikit-learn, MLflow, Gensim, NLTK, and TensorFlow or PyTorch.
• Experience deploying production-grade ML solutions on AWS, Azure, or GCP.
• Understanding of MLOps, model lifecycle management, monitoring, and drift detection.
• Experience developing LLM applications, RAG pipelines, and enterprise GenAI solutions.
• Strong understanding of ML algorithms, model evaluation, and data processing techniques.
• Graduate degree in Computer Science, Engineering, Statistics, Operations Research, or another quantitative discipline, or equivalent practical experience.Preferred Qualifications
• Experience with Apache Spark and distributed data processing.
• Hands-on experience with the Databricks platform.
• Experience building enterprise-grade AI/ML solutions in customer-facing environments.
• Strong communication, problem-solving, and stakeholder management skills.Experience Requirements

1. Senior ML Engineer

• 4+ years of relevant industry experience.
• Strong hands-on expertise in Python, machine learning, and production ML deployment.
• Experience with MLOps, model monitoring, and LLM/RAG solutions preferred.2. ML Architect
• 6+ years of relevant industry experience.
• Proven experience designing end-to-end ML architectures and production AI systems.
• Strong expertise in cloud-based ML platforms, MLOps, and LLM/RAG architecture.
• Ability to advise stakeholders on technical strategy, architecture, and implementation best practices.Ideal Candidate Profile

A hands-on ML professional who can build and productionize machine learning and Generative AI solutions, with strong Python, cloud, and MLOps expertise. For the architect position, candidates should additionally demonstrate end-to-end architecture ownership and the ability to guide technical teams and customers.