Machine Learning Engineer

videosdk.live; Ghaziabad District; On-site; INR 550,000 - 1,100,000; Full time

Job summary

VideoSDK is seeking a Machine Learning Engineer in Surat (Onsite) to build and optimize ML models that power real-time communication features. You will work with Python, NumPy, Pandas, and CUDA, perform data preprocessing, feature engineering, and model evaluation, and collaborate with cross-functional teams to translate business needs into data-driven solutions.

The role emphasizes hands-on model development, experimentation, and deployment into production, with a focus on ASR/ML pipelines,

Qualifications

• BE/BTech in CS/IT with AI/ML specialization.
• 0–2 years of hands-on ML experience.
• Strong Python coding and data analysis skills.

Skills

Python programming

Statistics and ML fundamentals

Model training and evaluation

Data preprocessing and EDA

Experimentation and metrics

Git and APIs

Production model deployment

Education

BE/BTech in CS/IT

AI/ML focus

Tools

NumPy

Pandas

CUDA

Embeddings

Vector databases

LLMs

Job description

About VideoSDK

VideoSDK is a real-time communication platform that helps developers and businesses build scalable video, voice, and interactive experiences into their applications. With powerful APIs and SDKs, VideoSDK enables teams to create custom solutions for video conferencing, live streaming, webinars, virtual classrooms, and collaborative applications without worrying about the complexities of infrastructure.

Position

Machine Learning Engineer

Surat, GJ (Onsite)

Experience: 0 - 2 Years

Website: www.videosdk.live

Education: BE/BTech in CS/IT, AI/ML only.

Requirements

Must to have

• Strong proficiency in Python, including NumPy, Pandas, Nvidia CUDA.
• Strong understanding of statistics, probability, linear algebra, & machine learning fundamentals.
• Hands-on experience building, training, evaluating, and optimizing machine learning models / or LLMs.
• Experience with data preprocessing, feature engineering, exploratory data analysis (EDA), and model evaluation.
• Ability to define evaluation metrics, conduct experiments, and interpret model performance.
• Familiarity with Git, APIs, and deploying models into production environments.
• Strong problem-solving skills and the ability to translate business requirements into data-driven solution.

Good to have

• Familiarity with speech processing, ASR, TTS, or conversational AI.
• Familiarity with inferencing frameworks.
• Experience with LLMs, embeddings, vector databases, & RAG pipelines.
• Knowledge of audio analytics, real-time communication data, or voice AI applications.
• Understanding of MLOps, model monitoring, and production model optimization.