AI/ML Engineer

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.