Machine Learning Engineer
Company: Videosdk.live
Website: Visit Website
Business Type: Startup
Company Type: Product
Business Model: B2B
Funding Stage: Seed
Industry: Software Development
Salary Range: ₹ 3-5 Lacs PA
Job Description
About VideoSDK
VideoSDK is a developer-focused platform that provides real-time audio, video, and AI communication infrastructure. Its APIs and SDKs enable developers to build scalable communication experiences and intelligent voice AI agents, including conversational AI, speech processing, and low-latency voice interactions.
The platform enables developers to integrate AI models, speech technologies, and conversational capabilities into applications without building the entire real-time communication infrastructure from scratch.
Role Overview
We are looking for a Machine Learning Engineer with strong Python programming skills, solid mathematical foundations, and hands-on experience developing, training, evaluating, and optimizing machine learning models or Large Language Models (LLMs).
The ideal candidate will work on data-driven AI solutions, model experimentation, performance evaluation, and production deployment. Experience in speech processing, audio intelligence, real-time conversational AI, or GPU-accelerated inference will be an added advantage.
Must To Have
- Strong proficiency in Python, including NumPy, Pandas, Nvidia CUDA.
- Strong understanding of statistics, probability, linear algebra, and 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 solutions.
Good To Have
- Familiarity with speech processing, ASR, TTS, or conversational AI.
- Familiarity with inferencing frameworks.
- Experience with Large Language Models (LLMs), embeddings, vector databases, and RAG pipelines.
- Knowledge of audio analytics, real-time communication data, or voice AI applications.
- Understanding of MLOps, model monitoring, and production model optimization.
Ideal Candidate Profile
A hands-on ML engineer who can move beyond experimentation and contribute to building, evaluating, and optimizing AI models for real-world applications. Candidates with experience in GPU acceleration, inference performance, speech technologies, or real-time AI systems will be particularly relevant.