Role Overview:
As an AI/ML Engineer at our partner company, 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.
