AI/ML Engineer
Marathahalli, Bangalore
3 years
Role Overview:
As an AI/ML Engineer at Plivo, you’ll play a hands-on role in building and scaling production-grade AI models that power our global communications platform. Working closely with product and engineering teams, you’ll design, train, and deploy models that solve real-world problems in speech, language, and voice automation at scale. This is a high-visibility, high-impact opportunity perfect for analytical, curious individuals who want to contribute meaningfully from Day 1.
Key Responsibilities
Train, fine-tune, and deploy AI/ML models for use cases like speech recognition, speaker isolation, and turn detection across languages and verticals.
Build scalable inference pipelines and integrate models seamlessly into the production environment.
Optimize models for latency, accuracy, and throughput for real-time, global-scale AI.
Analyze large datasets to identify patterns, surface improvements, and drive model performance.
Collaborate cross-functionally with engineering, product, and data teams to deliver production-ready AI features.
Explore and implement open-source frameworks, build internal AI tooling, and stay ahead of the curve.
Stay current with the latest trends in LLMs, generative AI, and voice intelligence because we’re always pushing the frontier.
What We’re Looking For
B.Tech in Computer Science, AI/ML, Data Science, or a related field from a top engineering school.
Strong theoretical understanding of ML algorithms, deep learning, and model training.
Proficiency in Python and experience with frameworks like PyTorch, TensorFlow, or Hugging Face Transformers.
Hands-on experience building models (e.g., NLP, ASR, TTS, embeddings, voice agents); Kaggle or competition experience is a strong plus.
Solid experience in data processing, feature engineering, and model evaluation techniques.
Analytical mindset, bias for action, and excellent communication and collaboration skills.
Nice to Have
Experience working in real-time systems and deploying models in production.
Familiarity with MLOps tools and pipelines.
Exposure to speech, voice, or multimodal datasets.
Contributions to open-source ML projects.
Experience building models from scratch.
Python coursePyTorch (Machine Learning Library)TensorFlowHugging Face Transformers+16
