About This Position

We are looking for a Sr. Machine Learning Engineer to develop and deploy machine learning solutions that drive impactful, data-driven insights across the delivery domain. This role combines hands-on model development with opportunities for strategic input, allowing you to tackle real-world challenges through advanced data science. The ideal candidate will be skilled in machine learning, deep learning, and model deployment by applying software development principles in wide range of applications such as forecasting, risk scoring, and more. You will work collaboratively to ensure these solutions seamlessly integrated into our systems with a focus on scalability and performance.

What You'll Bring

• Bachelors degree in Computer Science, Computer Engineering, or a related field. A Masters degree is a plus, preferably completed or close to completion.
• 3-5 years of hands-on experience in machine learning and model deployment.

• Strong proficiency with Python and Go languages.

• Experience on NoSQL (e.g., MongoDB, DynamoDB) and SQL databases (e.g., PostgreSQL).
• Experience with machine learning libraries like scikit-learn, xgboost/lightgbm/catboost, PyTorch, and/or TensorFlow.
• Experience in scaling applications with containers and container orchestration frameworks such as Kubernetes.

• Working on message queue and streaming systems like Kafka is big plus

• Working on cloud environments like AWS is big plus

• Good knowledge of Caching(Redis, etc)

• Have a strong DevOps mindset

• Proven proficiency in Continuous Delivery and Continuous Integration best practices
• Developing and maintaining services using frameworks such as FastAPI, Go Fiber.
• Previous experience working with GPS data & development and usage of location based services (OSRM, Valhalla, etc.) is a strong plus.

• Interest in applying research methodologies to practical, business-focused

  solutions.

• Excellent communication skills with the ability to collaborate effectively within cross-functional teams.

Your Responsibilities

• Develop, fine-tune, and deploy production level machine learning models and pipelines for diverse use cases (e.g., forecasting, risk scoring, sentiment analysis).

• Apply and optimize machine learning and deep learning models.

• Collaborate with cross-functional teams to build and deploy machine learning pipelines and APIs in an Agile working squad.
• Develop scalable models to support tasks such as forecasting, and risk analysis
• Explore academic research methodologies and adapt them to solve business-oriented challenges.

Benefits

• Hybrid working model

• Private Health Insurance (including spouse and children)

• Multinet Meal Card

• Gifts for Birth and Marriage Events

• Psychological Counseling Support

• Nutritionist Consultation

• Shuttle and Ring Service Availability