Machine Learning Engineer

Company: VideoSDK

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.