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Lead ML Engineer

Lead ML Engineer

ADA

0 years

0 Lacs

Bengaluru Karnataka India

Posted:1 day ago| Platform:

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Skills Required

ml learning drive development deployment cutting scalability efficiency reliability leadership design data model optimization monitoring integration aws gcp azure collaboration software research ai stack python tensorflow pytorch spark kubernetes docker hadoop sagemaker vertex management automation rest

Work Mode

On-site

Job Type

Full Time

Job Description

Job Title:

Lead ML Engineer

Location:

Bangalore, Karnataka

Company:

ADA GlobalWe are seeking a Lead Machine Learning Engineer to drive the development and deployment of cutting-edge ML systems. You will lead a team of ML engineers, collaborate with cross-functional teams, and ensure the scalability, efficiency, and reliability of our ML solutions. This role requires strong technical expertise, leadership, and a deep understanding of ML systems in production.

Key Responsibilities:

End-to-End ML Development – Design, build, and deploy scalable ML models for real-world applications.
Team Leadership – Guide and mentor a team of ML engineers and data scientists.
Model Optimization & MLOps – Implement model monitoring, retraining, and CI/CD pipelines for ML models.
Big Data & Cloud Integration – Work with large-scale data pipelines, distributed computing, and cloud services (AWS/GCP/Azure).
Cross-Functional Collaboration – Work closely with data engineers, product teams, and software developers.
Algorithm Innovation – Research and implement state-of-the-art ML and deep learning techniques.
Performance & Scalability – Ensure ML models are efficient and optimized for deployment in production environments.

Requirements:

Education: Bachelor's or master’s in computer science, AI, Data Science, or a related field.
ML Expertise: Strong background in Supervised, Unsupervised, and Deep Learning techniques.
Tech Stack: Python, TensorFlow, PyTorch, Scikit-learn, Spark, Kubernetes, Docker.
Data & Infrastructure: Experience with Big Data (Hadoop, Snowflake), Cloud ML tools (SageMaker, Vertex AI).
MLOps & CI/CD: Hands-on with ML lifecycle management, monitoring, and automation.
APIs & Deployment: Experience in deploying ML models via REST APIs and microservices.

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Lead ML Engineer