Job Summary
We are looking for a skilled Machine Learning Engineer to design, develop, and deploy scalable machine learning models and AI-driven solutions. The ideal candidate will have experience in building end-to-end ML pipelines, working with large datasets, deploying models into production, and collaborating with cross-functional teams to solve real-world business problems.
Key Responsibilities
- Design, develop, and deploy machine learning models for predictive analytics and intelligent automation.
- Build and maintain end-to-end ML pipelines, including data preprocessing, feature engineering, model training, evaluation, and deployment.
- Work closely with data scientists, software engineers, and business stakeholders to understand requirements and translate them into ML solutions.
- Develop and optimize supervised, unsupervised, and deep learning models.
- Deploy machine learning models using cloud platforms and MLOps best practices.
- Monitor model performance, retrain models as needed, and ensure production reliability.
- Build REST APIs or microservices to serve machine learning models.
- Perform data analysis and feature engineering to improve model accuracy.
- Document technical designs, model performance metrics, and deployment processes.
- Stay updated with the latest advancements in Machine Learning, AI, and Generative AI technologies.
Required Skills
- Strong programming experience in Python.
- Hands-on experience with machine learning libraries such as Scikit-learn, TensorFlow, PyTorch, or XGBoost.
- Experience with data manipulation using Pandas, NumPy, and SQL.
- Knowledge of supervised and unsupervised learning algorithms.
- Experience in feature engineering, model evaluation, and hyperparameter tuning.
- Familiarity with deep learning concepts and neural networks.
- Experience deploying ML models using FastAPI, Flask, or similar frameworks.
- Understanding of MLOps concepts, including model versioning, monitoring, and CI/CD.
- Experience with Git and version control.
- Strong analytical, problem-solving, and communication skills.
Preferred Skills
- Experience with Generative AI, Large Language Models (LLMs), or Retrieval-Augmented Generation (RAG).
- Knowledge of LangChain, LlamaIndex, or similar AI orchestration frameworks.
- Experience with vector databases such as Pinecone, FAISS, Milvus, or ChromaDB.
- Familiarity with NLP, Computer Vision, or Time Series forecasting.
- Experience with Docker and Kubernetes.
- Exposure to Apache Spark or distributed data processing frameworks.
- Experience with MLflow, Kubeflow, or similar MLOps tools.
Cloud & Tools
- Python
- SQL
- Scikit-learn
- TensorFlow / PyTorch
- Pandas
- NumPy
- FastAPI / Flask
- Git
- Docker
- Kubernetes
- MLflow
- Azure ML / AWS SageMaker / Google Vertex AI
- Databricks (Preferred)
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Statistics, Mathematics, or a related field.
- Relevant certifications in Machine Learning or Cloud platforms are a plus.
Preferred Experience
- 4–8 years of experience in Machine Learning or AI application development.
- Experience building and deploying production-grade ML models.
- Hands-on experience with cloud-based ML services.
- Experience working in Agile/Scrum development environments.
Nice to Have
- Experience with recommendation systems, fraud detection, forecasting, or predictive analytics.
- Knowledge of data engineering concepts and ETL pipelines.
- Experience integrating ML models with enterprise applications through REST APIs.
- Contributions to open-source ML projects or published research in AI/ML.
What We Offer
- Opportunity to work on cutting-edge AI and Machine Learning projects.
- Collaborative and innovative work environment.
- Career growth and learning opportunities.
- Competitive salary and comprehensive benefits.
- Flexible work arrangements (Remote/Hybrid, based on business needs).
