About Senzcraft :
Founded by IIM Bangalore and IEST Shibpur Alumni, Senzcraft is a hyper-automation company. Senzcraft vision is to Radically Simplify Today's Work. And Design Business Process For The Future. Using intelligent process automation technologies.
We have a suite of SaaS products and services, partnering with automation product companies.
Please visit our website - for more details
Our AI Operations SaaS platform –
Senzcraft on linkedin ->
Senzcraft is awarded by Analytics India Magazine in it’s report “State of AI in India” as a “Niche AI startup”. Senzcraft is also recognized by NY based SSON as a top hyper-automation solutions provider.
About the Role (Lead ML Engineer) :
Location : Bangalore – Hybrid
Experience : We have multiple roles open (6-12 years)
Employment Type : Full-time
Job Description :
We are looking for a Lead ML Engineer to design, build, and scale machine learning solutions . The ideal candidate will have strong hands-on experience with ML frameworks (TensorFlow, PyTorch, scikit-learn, Hugging Face Transformers) and a proven track record in ML Ops, model monitoring, and lifecycle management .
This role will also drive innovation in big data processing using Spark, Databricks, and Delta Live Tables , with a focus on cloud deployment on AWS .
Key Responsibilities
- Design, build, and deploy scalable ML systems leveraging TensorFlow, PyTorch, scikit-learn, and Hugging Face Transformers for supply chain and logistics–specific use cases (e.g., demand forecasting, route optimization, inventory management).
- Define and implement ML Ops best practices — CI / CD pipelines, model versioning, monitoring, and retraining workflows.
- Develop robust data pipelines and scalable solutions using Spark, Databricks, and Delta Live Tables .
- Collaborate with data scientists and software engineers to bring models from research to production.
- Optimize model performance, latency, and resource utilization in AWS cloud environments .
- Ensure end-to-end model lifecycle management , including governance, observability, and retraining strategies.
Mandatory Skills
Strong experience with TensorFlow, PyTorch, scikit-learn, and Hugging Face Transformers .Deep understanding of ML Ops practices — deployment, monitoring, and lifecycle management.Expertise in big data frameworks — Spark, Databricks, Delta Live Tables.Hands-on experience with AWS ML and data services (SageMaker, Lambda, EMR, S3, etc.).Proficiency in Python , version control ( Git ), and CI / CD tools .Good to Have
Exposure to containerization (Docker, Kubernetes) and infrastructure as code (Terraform) .Experience with data streaming (Kafka, Kinesis) or feature store frameworks .Knowledge of LLM optimization or deployment in production environments.Education
Bachelor’s or Master’s in Computer Science, Data Engineering, or a related field.