Senior Machine Learning Engineer

We are seeking a highly experienced Senior Machine Learning Engineer with more than 7 years of experience in AI/ML and strong expertise in Google Cloud Platform (GCP) services. As a core member of our team, you will design and implement scalable machine learning solutions in the Health Care Sector while working closely with stakeholders to deliver impactful AI-driven strategies. The ideal candidate will possess deep knowledge of machine learning frameworks, GCP services, and MLOps practices, ensuring seamless deployment and management of models in production environments.

Collaborate with stakeholders to gather requirements and design machine learning solutions tailored to business needs.

Architect and implement end-to-end AI/ML solutions leveraging GCP services like Vertex AI, AutoML, Dataflow, Pub/Sub, and BigQuery.

Design and develop scalable data pipelines and orchestration frameworks using Apache Airflow and other big data technologies (e.g., Hadoop, Spark).

Handle streaming data processing using Kafka and optimize real-time data models for AI systems.

Implement feature engineering, model training, evaluation, and deployment with state-of-the-art ML frameworks such as TensorFlow and scikit-learn.

Apply deep learning architectures such as CNNs, RNNs, and Transformers, with a particular focus on Natural Language Processing (NLP).

Ensure robust version control and continuous integration/continuous deployment (CI/CD) for data projects, following MLOps best practices.

Optimize data modeling, schema design, and warehousing strategies to manage large-scale datasets.

Work on data extraction, transformation, and loading (ETL) processes, ensuring data accuracy and availability for ML models.

ML Frameworks: Proficiency in TensorFlow and scikit-learn for developing machine learning models.

GCP ML Services: Expertise in Vertex AI, AutoML, Dataflow, BigQuery, Pub/Sub, Kubernetes, Kafka, and Dataproc.

Big Data Technologies: Hands-on experience with Hadoop and Spark for processing and managing large datasets.

Feature Engineering and Selection: Skilled in creating optimized features to improve model performance.

Deep Learning: Strong understanding of deep learning architectures, including CNNs, RNNs, and Transformers.

NLP Expertise: Experience in applying Natural Language Processing techniques.

Data Pipeline Design: Knowledge of data pipeline orchestration tools like Apache Airflow and streaming data frameworks.

MLOps: Proficient in version control (Git) and CI/CD pipelines to streamline model deployment and maintenance.

Data Warehousing and ETL: Experience with schema design and ETL processes for large-scale datasets.

7+ years of experience in AI/ML, with at least 3 years focused on Google Cloud Platform services.

Proficiency in model development, deployment, and monitoring in production environments.

Strong background in data modeling, system architecture, and MLOps practices.

Demonstrated experience working with streaming data and building real-time machine learning systems.

Advanced degree in Computer Science, Machine Learning, Data Science, or a related field is preferred.

Google Cloud Professional Machine Learning Engineer certification is a plus.

Strong communication and collaboration skills to work effectively with cross-functional teams and stakeholders.

Ability to drive technical discussions and decisions in fast-paced environments.

Problem-solving mindset with the ability to architect scalable solutions.

Opportunities for professional growth and leadership in AI and ML technologies.

Collaborative and innovative culture focused on the future of AI and data-driven solutions in Health Care Domain.