Role overview
Role Overview
We are looking for a hands-on Machine Learning Engineer (5+ years experience) who can design, develop, and optimize machine learning models and pipelines in a cloud-native environment.
The role requires strong programming fundamentals, proficiency in Python and ML frameworks (such as TensorFlow, PyTorch, or scikit-learn), as well as experience deploying ML models and pipelines in AWS, ensuring scalability and integration with production systems.
The engineer should be comfortable working independently on assigned tasks in an agile product engineering setup, with a focus on scalability, performance, and reliability of ML solutions.
- Core Technical Skills (Must-Have)
A. Machine Learning
5+ years of hands-on experience in Machine Learning model development and deployment
Proficient in:
o Python (primary language for ML)
o ML frameworks: TensorFlow, PyTorch, or scikit-learn
o Data processing libraries: Pandas, NumPy
Data science fundamentals (feature engineering, model evaluation metrics, hyperparameter tuning)
Familiarity with model validation metrics, including data drift metrics (e.g., population stability index, Kolmogorov-Smirnov test) and model drift metrics (e.g., F1 score, ROC AUC score, RMSE).
B. MLOps & Deployment
The engineer should understand and have worked on:
Building and deploying ML models as containerized services or APIs via AWS SageMaker and frameworks like FastAPI, Flask, or similar
Model lifecycle management (training, validation, deployment)
Containerization (Docker) and orchestration
Observability for ML services (logging, metrics, monitoring)
Versioning of models and datasets Data pipelines for feature engineering and preprocessing
CI/CD for ML model (GitLab CI + MLflow integration)
Good to have: Experience with Databricks for scalable data and ML workflows
C. Databases & Storage
Strong experience with relational databases: PostgreSQL or MySQL
Good understanding of:
o Writing SQL queries
o Index usage
o Joins, transactions
Experience with Vector DBs
Exposure to Redis or NoSQL DBs is a plus
- Engineering Best Practices (Mandatory)
The engineer must demonstrate ability to follow standard engineering practices:
Write clean, maintainable, well-structured code
Mandatory experience with:
o Code reviews (as reviewer and reviewee)
o Git workflows
Understanding of software engineering concepts, including classes, functions, logging
and monitoring, error handling & exception design and unit tests.
- API Development & Integration Skills
Ability to design and implement REST APIs
Experience documenting APIs using Swagger/OpenAPI
Knowledge of:
o Request/response models
o Validation frameworks
o API versioning basics
- Performance, Reliability & Security Expectations
(At a depth appropriate for 5+ XP years) Performance
Understand impact of:
o DB queries
o Caching
o Pagination
Understand asynchronous processing basics
Reliability
Should know:
o Retry logic
o Basic resiliency patterns (timeouts, fallbacks)
Security
Knowledge about API security
o Auth/Auth (Oauth2)
o Rate limiting and input validation
Familiarity with data privacy compliance (GDPR, encryption)
Must follow secure coding practices:
o Input sanitization
o Avoiding SQL injection
o Secure API communication
- Domain Knowledge (Not Required, but Good to Have)
The engineer is not expected to have domain expertise, but experience in the following is
beneficial:
Supply Chain Management
Logistics
Inventory or warehouse systems
If the domain is unfamiliar, the engineer must be able to learn quickly with guidance.
- Soft Skills & Collaboration Expectations
Good English proficiency
Clear communication and ability to explain technical decisions Ability to estimate tasks and deliver within timelines
Proactiveness in asking questions when needed
Ability to work in Agile teams (Scrum/Kanban)
Collaboration across time zones
Regular participation in:
o Refinements
o Stand-ups
o Sprint reviews
o Retrospectives
The engineer should be dependable and capable of completing assigned work independently
once requirements are given.
Expected Deliverables
ML model deployment and serving via containerized services and APIs including
establishment of required CI/CD pipelines
Code adhering to project standards & guidelines
Bug fixes, enhancements, and production support as needed
Accurate status updates and timely delivery in sprints
Clear documentation for services and APIs
Experience Requirement
Overall Experience: 5+ years
Should have worked as:
o ML Engineer
o MLOps Engineer
- Good-to-Have Certifications
(Not mandatory)
AWS Certified Machine Learning Specialty.