Title: Machine Learning Engineer
Location: Gurgaon, Haryana (Onsite/Hybrid)
Experience:
- 4-6 years of hands-on experience in Machine Learning Engineering, Applied Machine Learning, or related roles.
- Experience with Pandas, NumPy, Scikit-learn, and related Python libraries.
- Hands-on experience with Large Language Models (LLMs).
- Strong proficiency in Python.
About the Role:
We are seeking a highly motivated Machine Learning Engineer with 4-6 years of experience to design, build, deploy, and optimize scalable machine learning solutions that solve real-world business problems. The ideal candidate has hands-on experience in developing production-grade ML models, implementing MLOps best practices, and collaborating with cross-functional teams to deliver AI-driven products.
Key Responsibilities:
Machine Learning Development:
- Design, develop, train, and optimize Machine Learning and Deep Learning models for classification, regression, forecasting, NLP, and computer vision applications.
- Perform feature engineering, model selection, hyperparameter tuning, and performance evaluation.
- Conduct experiments and improve model accuracy, scalability, and reliability.
Model Deployment & MLOps:
- Deploy, monitor, and maintain ML models in production environments.
- Build and manage end-to-end ML pipelines using MLOps best practices.
- Implement CI/CD workflows for machine learning applications.
- Containerize applications using Docker and orchestrate deployments with Kubernetes.
Data Engineering & Processing:
- Work with structured and unstructured datasets to build scalable data pipelines.
- Process and analyze large datasets using SQL and distributed data processing tools.
- Collaborate with data engineering teams to ensure high-quality data availability.
Cross-functional Collaboration:
- Partner with Data Scientists, Product Managers, Backend Engineers, and Business stakeholders to understand requirements and deliver ML-powered solutions.
- Translate business challenges into scalable machine learning applications.
Model Monitoring & Optimization:
- Monitor model performance, latency, drift, and reliability in production.
- Continuously improve deployed models through retraining and optimization.
- Implement logging, monitoring, and alerting mechanisms for ML systems.
Research & Innovation:
- Stay updated with the latest advancements in Machine Learning, Deep Learning, Generative AI, and MLOps.
- Evaluate and integrate new frameworks, tools, and best practices into existing workflows.
Required Skills & Experience:
- 4-6 years of hands-on experience in Machine Learning Engineering, Applied Machine Learning, or related roles.
- Strong proficiency in Python.
- experience with Pandas, NumPy, Scikit-learn, and related Python libraries.
- Hands-on experience with: TensorFlow, PyTorch, Keras, XGBoost.
- Practical experience with one or more cloud platforms: AWS, Google Cloud Platform (GCP), Microsoft Azure
- Experience with Amazon SageMaker, Vertex AI, or Azure Machine Learning is an added advantage.
- Experience with: Docker, Kubernetes, FastAPI or Flask, CI/CD pipelines for ML applications, Model versioning and deployment strategies.
- Strong knowledge of SQL and NoSQL databases.
- Familiarity with Apache Spark and Hadoop is preferred.
- Experience processing large-scale datasets.
Preferred Skills:
- Hands-on experience with Large Language Models (LLMs).
- Experience using LangChain, LlamaIndex, or similar orchestration frameworks.
- Knowledge of Vector Databases such as Pinecone, Weaviate, or Milvus.
- Experience in NLP, Computer Vision, Recommendation Systems, or Generative AI applications.
- Familiarity with experimentation frameworks, A/B testing, and model evaluation methodologies.
Educational Qualification:
- Bachelor's or Master's degree (B.Tech/M.Tech) in Computer Science, Artificial Intelligence, Data Science, Information Technology, or a related field.
