Machine Learning Engineer

Location: India
Experience: 4-6 Years
Salary: 15 to 22 LPA

About the Role

We are looking for a highly skilled Machine Learning Engineer to join our team and contribute to building intelligent, scalable, and production-ready AI solutions across our products. The ideal candidate will have hands-on experience with developing ML models, working with large datasets, and deploying AI-driven features into real-world applications.

Key Responsibilities:

• Develop, train, and optimize machine learning models for various product use cases (recommendation systems, NLP features, predictive analytics,classification models, etc.).
• Implement end-to-end ML pipelines—from data preprocessing to model training, evaluation, deployment, and monitoring.
• Work closely with backend, product, and data teams to integrate ML solutions into production.
• Improve existing AI features like candidate job matching, smart sourcing, automated job applications, CV parsing, and intelligent chat interactions.
• Conduct A/B testing and model performance evaluation to ensure high accuracy and reliability.
• Research and experiment with new algorithms, frameworks, and AI advancements to enhance product capabilities.
• Optimize models for efficiency, scalability, and low-latency real-time performance.
• Maintain clear documentation and follow best coding, security, and data-handling practices.

Required Skills & Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI, Machine Learning, or a related field.
• 3+ years of hands-on experience in ML model development and deployment.
• Strong proficiency in Python and ML/data libraries such as TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy.
• Experience with NLP libraries (Transformers, spaCy, NLTK) is a strong advantage.
• Solid understanding of data structures, algorithms, probability, and statistics.
• Experience building and using APIs for inference (FastAPI, Flask, etc.).
• Knowledge of cloud services (AWS/GCP/Azure) and containerization (Docker).
• Experience with vector databases, embeddings, or LLM-based applications is a plus.
• Familiarity with MLOps tools such as MLflow, SageMaker, Kubeflow, or similar.

Preferred Qualifications:

• Experience with recommendation engines or talent-matching systems.
• Understanding of prompt engineering and LLM fine-tuning.
• Experience in deploying AI-powered features in production environments.

Soft Skills:

• Strong analytical and problem-solving skills.
• Ability to work independently and in a fast-paced environment.
• Excellent communication and documentation skills.
• Strong ownership mentality and focus on delivery.