Lead Machine Learning Engineer

Direct Onboarding – US Company
Job Type: Full Time
Job Location: Remote

Duties & Responsibilities

• Design, develop, and deploy end-to-end machine learning models and pipelines for production environments.
• Lead the full ML lifecycle — from data ingestion and feature engineering to model training, evaluation, and monitoring.
• Collaborate with cross-functional teams including product, engineering, and data teams to define ML requirements and deliver scalable solutions.
• Architect and maintain ML infrastructure using cloud-based platforms such as AWS, GCP, or Azure.
• Research and evaluate state-of-the-art ML techniques, frameworks, and tools to continuously improve model performance.
• Mentor and guide junior ML engineers, contributing to overall team capability development.
• Establish best practices for model versioning, testing, documentation, and deployment.
• Present findings, progress, and technical strategies to both technical and non-technical stakeholders.

Our Requirements

• Must be able to work between 8:00 AM – 5:00 PM Eastern Time Zone (ET).
• Strong communication skills in English and the ability to collaborate effectively across teams are essential.
• 3–5 years of hands-on experience in machine learning, data science, or a related field.
• Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Hands-on expertise with Large Language Models (LLMs), including fine-tuning, prompt engineering, and model evaluation.
• Solid experience building Retrieval-Augmented Generation (RAG) pipelines and integrating them into production applications.
• Practical knowledge of agentic AI frameworks and multi-agent system design (e.g. LangChain, LangGraph, AutoGen, or similar).
• Experience working with vector databases such as Pinecone, Weaviate, Qdrant, or pgvector.
• Familiarity with MLOps practices — model versioning, CI/CD pipelines, and automated retraining workflows.
• Experience with cloud platforms such as AWS SageMaker, GCP Vertex AI, or Azure ML.
• Strong understanding of data structures, algorithms, and statistical modelling.
• Experience with tools such as MLflow, Kubeflow, Docker, or Kubernetes is an advantage.
• Demonstrated ability to lead technical projects and mentor team members.
• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related discipline.

What You’ll Receive

• Competitive compensation package aligned with experience and market benchmarks.
• Direct onboarding with a reputable US-based company.
• Fully remote work environment with flexible tooling and resources.
• Opportunity to work on cutting-edge AI/ML products with a high-impact global team.
• Professional development support and exposure to an international client base.
• Collaborative and growth-oriented team culture.