Machine Learning Engineer

About Us

We're a fast-moving startup on a mission to revolutionize the call centre industry using the power of AI and Large Language Models. We’re building intelligent systems to enhance customer experience, supercharge agent performance, and drive operational excellence.

Through advanced conversational AI, we build systems that understand, adapt, and evolve with every conversation. Our platform powers decision-support tools and intelligent virtual agents, helping call centers deliver faster, smarter, and more human customer service—where every interaction matters.

As a Data/ML Engineer on our founding team, you’ll design and maintain the pipelines that transform messy, real-world customer data into clean, reliable datasets. You’ll also take ownership of training, deploying, and monitoring models that bring conversational AI into production. This hybrid role is ideal for someone who enjoys both building strong data foundations and shipping AI systems that scale.

What You’ll Do

1. Data Engineering

• Design and maintain scalable pipelines to ingest, clean, and normalize omnichannel call center data (voice, chat, email, SMS).
• Ensure high data quality, reliability, and accessibility for downstream ML and analytics.
• Collaborate with data scientists to prepare annotated datasets for training and evaluation.

2. Machine Learning Engineering

• Train, deploy, and monitor ML/LLM models in production (batch + real-time).
• Implement model versioning, experiment tracking, and retraining pipelines.
• Optimize inference for speed, scalability, and cost-efficiency.
• Build feedback loops to continuously improve model performance.

3. Collaboration

• Work closely with product, engineering, and data science teams members
• Document and communicate your work clearly to both technical and non-technical stakeholders.

What You Bring

• 3–6 years of experience as a Data Engineer, ML Engineer, or similar hybrid role.
• Deep expertise in NLP and LLMs.
• Strong skills in Python and SQL.
• Experience building ETL/ELT pipelines with tools like Airflow, dbt, or Spark.
• Hands-on experience training and deploying ML models (e.g., scikit-learn, PyTorch, TensorFlow).
• Familiarity with cloud platforms (AWS, GCP, or Azure).
• Knowledge of MLOps practices (model monitoring, experiment tracking, CI/CD for ML).
• Comfort working with messy, real-world, multi-channel data.

Bonus Points For

• Experience in or knowledge of the call centre / contact centre space.
• Familiarity with voice-to-text models, real-time analytics, or conversation intelligence.
• MLOps experience (e.g., model versioning, monitoring, CI/CD).