Machine Learning Engineer

Machine Learning Engineer – Applied AI & Scalable Model Deployment

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Darwix AI is redefining how large-scale human interactions drive revenue outcomes. As we expand rapidly across India, MENA, and Southeast Asia, we are strengthening our core ML engineering team to accelerate new feature development and production deployments.

Machine Learning Engineer

You will collaborate closely with AI research engineers, backend teams, and product managers to translate business problems into scalable and maintainable ML pipelines. This is a hands-on, impact-first role focused on turning advanced ML models into production systems used by large enterprise teams daily.

• Design, build, and optimize models for tasks such as classification, scoring, topic detection, and conversation summarization.
• Work on feature engineering pipelines, data preprocessing, and large-scale training on structured and unstructured datasets.
• Evaluate model performance using robust metrics (accuracy, recall, precision, WER for voice tasks).

• Package and deploy models as scalable APIs and microservices integrated with core product workflows.
• Optimize inference pipelines for latency, throughput, and cost in production environments.
• Work closely with DevOps and backend engineers to ensure robust CI/CD, monitoring, and auto-recovery workflows.

• Develop and maintain data pipelines to ingest, clean, transform, and label large volumes of voice and text data.
• Implement logging, data versioning, and audit trails to ensure traceable and reproducible experiments.

• Build automated evaluation frameworks to detect model drift and performance degradation.
• Analyze live production data to identify opportunities for iterative improvements and fine-tuning.
• Contribute to A/B testing design for model-driven features to validate business impact.

• Work with cross-functional teams to gather requirements, define success criteria, and drive end-to-end feature implementation.
• Maintain clear technical documentation for data flows, model architectures, and deployment processes.
• Mentor junior engineers on best practices in ML system design and operationalization.

• 2–6 years of experience in ML engineering, applied ML, or data science with a strong focus on production systems.
• Proficiency in

  Python

  , including experience with ML libraries such as PyTorch, TensorFlow, Scikit-learn, or Hugging Face.
• Solid understanding of data preprocessing, feature engineering, and ML model lifecycle management.
• Experience deploying models as REST APIs or microservices in cloud or containerized environments.
• Strong knowledge of relational and NoSQL databases, and familiarity with data pipeline tools.
• Good understanding of MLOps concepts, including CI/CD for ML, model monitoring, and A/B testing.

• Exposure to

  speech or voice analytics

  , including speech-to-text systems and audio signal processing.
• Familiarity with large language models (LLMs), embeddings, or retrieval-augmented generation (RAG) pipelines.
• Experience with distributed training, GPU optimization, or large-scale batch inference.
• Knowledge of vector databases (FAISS, Pinecone) and real-time recommendation systems.
• Prior experience in SaaS product environments targeting enterprise clients.

• Models integrated into production systems delivering measurable improvements to business KPIs.
• High availability, low-latency inference pipelines powering real-time features for large enterprise users.
• Rapid iteration cycles from model conception to production deployment.
• Strong, well-documented, and reusable ML infrastructure supporting ongoing product and feature launches.

• Are passionate about building ML systems that create real business impact, not just offline experiments.
• Enjoy working with noisy, multilingual, and large-scale datasets in high-stakes settings.
• Love solving engineering challenges involved in scaling AI solutions to thousands of enterprise users.
• Thrive in a fast-paced, ownership-driven environment where ideas translate quickly to live features.
• Value documentation, reproducibility, and collaboration as much as technical depth.

careers@darwix.ai

Application – Machine Learning Engineer – [Your Name]

(Optional): Include links to your GitHub, published papers, blog posts, or a short note on a real-world ML system you helped deploy and what challenges you overcame.

This is a unique opportunity to join the core engineering team at one of India’s most innovative GenAI startups and shape how enterprise teams leverage AI for real-time decision-making and revenue growth. If you are ready to build AI at scale, Darwix AI wants to hear from you.