Machine Learning Engineer

While technology is the heart of our business, a global and diverse culture is the heart of our success. We love our people and we take pride in catering them to a culture built on transparency, diversity, integrity, learning and growth.


If working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi!

About Quantiphi 

Quantiphi is an award-winning Applied AI and Big Data software and services company, driven by a deep desire to solve transformational problems at the heart of businesses. Our signature approach combines groundbreaking machine-learning research with disciplined cloud and data-engineering practices to create breakthrough impact at unprecedented speed.

Company Highlights:

Quantiphi has seen 2.5x growth YoY since its inception in 2013, we don’t just innovate—we lead. Headquartered in Boston, with 4000+ Quantiphi professionals across the globe. As an Elite/Premier Partner for Google Cloud, AWS, NVIDIA, Snowflake, and others, we’ve been recognized with:

• 17x Google Cloud Partner of the Year awards in the last 8 years

• 3x AWS AI/ML award wins

• 3x NVIDIA Partner of the Year titles

• 2x Snowflake Partner of the Year awards

• We have also garnered Top analyst recognitions from Gartner, ISG, and Everest Group.

• We have been certified as a Great Place to Work for the third year in a row- 2021, 2022, 2023.

Be part of a trailblazing team that’s shaping the future of AI, ML, and cloud innovation. Your next big opportunity starts here!

Job Overview:

We are looking for a Machine Learning Engineer with strong expertise in Google Cloud AI tools, ML model development, and end-to-end deployment. The ideal candidate will have hands-on experience with Google Cloud Document AI, Vertex AI, and Large Language Models (LLMs). You will be responsible for designing, training, evaluating, and fine-tuning ML models, integrating them with cloud-based applications, and ensuring scalable and reliable performance in production environments.

Key Responsibilities:

• Design, develop, train, and fine-tune machine learning models, including custom and pre-trained models on Google Cloud Vertex AI and Document AI.

• Build and manage custom Document AI processors such as Custom Document Splitter, Custom Document Classifier, and Custom Document Extractor.

• Work with pre-trained Document AI processors and customize them for business-specific document understanding tasks.

• Develop and deploy ML solutions using GCP services like Cloud Functions, Cloud Run, Firestore, Cloud SQL, Cloud Storage, and BigQuery.

• Design and implement data preprocessing pipelines for large-scale, unstructured, and semi-structured data.

• Integrate ML models into production systems via secure and scalable APIs.

• Evaluate model performance using standard ML metrics, perform model validation, and optimize for accuracy, latency, and efficiency.

• Collaborate with cross-functional teams (Data Engineers, Software Developers, and Product Teams) to ensure seamless model integration and delivery.

• Troubleshoot and debug ML pipelines, training jobs, and model deployment issues.

• Maintain proper version control of code, models, and configurations using Git/GitHub.

• Follow best practices for ML lifecycle management, testing, and documentation.

Basic Qualifications (Essential):

• Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field, or equivalent practical experience.

• Proven experience with Google Cloud Document AI (Custom Workbench: Splitter, Classifier, Extractor, and pre-trained processors).

• Hands-on experience with Google Cloud Vertex AI for model training, tuning, and deployment.

• Strong understanding and practical experience with Large Language Models (LLMs) and their fine-tuning.

• Proficiency in Python and ML libraries/frameworks (e.g., TensorFlow, PyTorch, scikit-learn).

• Experience with ML model design, training, testing, evaluation, and fine-tuning.

• Solid experience in data preprocessing and feature engineering.

• Familiarity with GCP services such as Cloud Functions, Cloud Run, Firestore, Cloud Storage, Cloud SQL, and BigQuery.

• Strong understanding of API integration for ML model deployment.

• Proficiency in troubleshooting and debugging ML-related issues.

• Experience with Git/GitHub for version control and collaboration.

Other Qualifications (Good to Have):

• Knowledge of MLOps practices for automating ML workflows, model versioning, and continuous deployment.

• Experience building and exposing ML models via FastAPI or similar frameworks.

• Familiarity with data pipeline orchestration tools (e.g., Airflow, Kubeflow).

• Understanding of security and compliance best practices in ML systems.

• Strong analytical, problem-solving, and communication skills.

What is in it for you:

• Be part of the fastest-growing AI-first digital transformation and engineering company in the world

• Be a leader of an energetic team of highly dynamic and talented individuals

• Exposure to working with fortune 500 companies and innovative market disruptors

• Exposure to the latest technologies related to artificial intelligence and machine learning, data and cloud