Senior Machine Learning Engineer
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.