About Logile

Logile is the leading retail labor planning, workforce management, inventory management and store execution provider deployed in thousands of retail locations across North America, Europe, Australia, and Oceania.  

Our proven AI, machine-learning technology and industrial engineering accelerate ROI and enable operational excellence with improved performance and empowered employees. Retailers worldwide rely on Logile solutions to boost profitability and competitive advantage by delivering the best service and products at optimal cost.  

From labor standards development and modeling to unified forecasting, storewide scheduling, and time and attendance, to inventory management, task management, food safety, and employee self-service — we transform retail operations with a unified store-level solution. Gain the Advantage with The Logic of Retail. One Platform for store planning, scheduling and execution.  

For more information, visit www.logile.com 

Job Summary:  

We are looking for a Machine Learning Engineer (MLE) who can take ML models from idea to production reliably. 

This is not a research-heavy role. The focus is on: 

• Building robust ML pipelines  

• Deploying models into real-world systems  

• Ensuring scalability, monitoring, and performance  

You will work closely with Data Scientists, Data Engineers, and Product teams to ensure ML solutions are usable, reliable, and impactful. 

Key Responsibilities: 

ML System Design & Deployment 

• Build and deploy end-to-end ML pipelines (training → validation → deployment → monitoring)  

• Convert notebooks and prototypes into production-grade services  

• Design batch and real-time inference systems 

MLOps & Infrastructure 

• Implement CI/CD pipelines for ML workflows  

• Work with tools like:  

• MLflow / Weights & Biases  

• Airflow / Prefect  

• Docker / Kubernetes  

• Manage model versioning, reproducibility, and experiment tracking 

Data Pipeline Integration 

• Collaborate with data engineering teams to:  

• Build feature pipelines  

• Ensure data quality and consistency  

• Work with structured and unstructured data 

Model Performance & Monitoring 

• Set up monitoring for:  

• Data drift  

• Model drift  

• Latency and system failures  

• Define SLAs for model performance 

Optimization & Scaling 

• Optimize models for:  

• Latency  

• Cost  

• Throughput  

• Work on inference optimization techniques (quantization, batching, caching) 
   

Job Location & Schedule:  

• This job is an onsite job at Logile Bhubaneswar Office. 

• It is expected that the selected candidate will be available to work with some hours of overlap with US working times 

Required Skills & Experience: 

• 5–10 years in ML Engineering / Software Engineering / Data Engineering roles  

 Hands-on experience deploying ML models into production 
 
Technical Skills 

Core 

• Strong Python skills  

• Experience with ML frameworks (Scikit-learn, TensorFlow, PyTorch)  

MLOps & Systems 

• Experience with:  

• Docker  

• REST APIs (FastAPI / Flask)  

• Cloud platforms (AWS / GCP / Azure)  

• Familiarity with feature stores and model registries  

Data 

• Strong SQL skills  

• Experience with data pipelines and ETL workflows 

System Thinking 

• Understanding of:  

• Latency vs accuracy trade-offs  

• Batch vs real-time systems  

• Failure handling and retries 

Preferred Skills 

• Experience with LLM-based systems (RAG pipelines, embeddings)  

• Exposure to vector databases (FAISS, Pinecone, Weaviate)  

• Experience with streaming systems (Kafka) 

Success in This Role Looks Like: 

• ML models are deployed and used in production  

• Pipelines are stable, monitored, and reproducible  

• Reduced time from experimentation → production  

• Minimal firefighting due to robust systems  

Compensation and Benefits: 

• The compensation and benefits associated for this role is benchmarked against the best in industry and job location. 

• Standard shift: 1 PM – 10 PM (shift allowance applicable as per role). 

• Shifts starting after 4 PM: Eligible for food allowance/subsidized meals and cab drop. 

• Shifts starting after 8 PM: Eligible for cab pickup as well.