About The Opportunity
We operate in the Information Technology & Services sector, focused on AI/ML product engineering and cloud-native data solutions for enterprise and SMB clients. Our teams design, build, and deploy production-grade machine learning systems that drive business outcomes across analytics, automation, and intelligent products.
This remote role (India) is an opportunity to join a fast-moving engineering organization delivering end-to-end ML solutionsmodel development, CI/CD-driven deployment, monitoring, and continual improvementacross cloud environments.
Role & Responsibilities
- Design, implement, and productionize ML models and inference pipelines with reliability, scalability, and observability in mind.
- Develop clean, well-tested Python services for data preprocessing, feature engineering, model training, and inference.
- Containerize models and services using Docker and integrate with cloud-hosted deployment pipelines on AWS.
- Collaborate with data scientists and product teams to translate proofs-of-concept into production-ready systems and APIs.
- Implement MLOps best practicesautomated training, model versioning, deployment strategies, monitoring, and alerting.
- Drive performance tuning and optimization for model latency, throughput, and cost across production workloads.
Skills & Qualifications
Must-Have
- Python
- PyTorch
- TensorFlow
- Scikit-learn
- SQL
- Docker
- AWS
Preferred
- MLflow
- Kubernetes
- Apache Spark
Qualifications
- Bachelor's degree in Computer Science, Engineering, Mathematics, or equivalent practical experience.
- Approximately 4 years of hands-on experience in machine learning engineering or related software engineering roles.
- Proven track record deploying ML models to production and maintaining model lifecycle (training, versioning, monitoring).
- Comfort working in remote, cross-functional teams and communicating technical trade-offs to stakeholders.
Benefits & Culture Highlights
- Fully remote role with flexible working hours and India-based hiring.
- Opportunity to own end-to-end ML features and grow into senior technical roles across product & cloud engineering.
- Learning budget and time for upskilling in MLOps, cloud platforms, and advanced ML techniques.
Primary role title (standardized): Machine Learning Engineer
To apply, candidates should be prepared to share code samples, model artefacts, and examples of deployed ML systems.
Skills: nlp,ml,llm
