Lead ML Engineer (6-12 years) - Supply chain and Logistics domain

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 – on linkedin ->

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 for supply chain and logistics applications . 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 .

Candidates with prior experience in the supply chain and logistics domain are mandatory.

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 .

Domain experience in supply chain and logistics is mandatory .

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.