Machine Learning Engineer

**Job Title: Machine Learning Engineer (Databricks) – 2–4 Years Experience** **Job Summary** We are looking for a **Machine Learning Engineer** with **2–4 years of experience** to design, develop, and deploy AI/ML solutions on the **Databricks Data Intelligence Platform** . The ideal candidate should have hands-on experience building machine learning models, developing ML pipelines, and deploying production-ready AI solutions using Python and modern ML frameworks. This role involves working closely with Data Engineers, Data Scientists, and business stakeholders to deliver scalable AI-driven solutions. **Key Responsibilities** - Design, develop, train, and deploy machine learning models to solve business problems. - Build end-to-end AI/ML solutions using the **Databricks Data Intelligence Platform** . - Develop scalable machine learning pipelines using **Python** , **PySpark** , and **SQL** . - Perform data preprocessing, feature engineering, and dataset preparation for model training. - Train, evaluate, fine-tune, and optimize machine learning models for performance and accuracy. - Utilize **MLflow** for experiment tracking, model versioning, and lifecycle management. - Deploy machine learning models to production and monitor model performance. - Build and optimize data pipelines required for AI and machine learning workloads. - Collaborate with Data Engineers, Data Scientists, and business stakeholders to understand business requirements and translate them into AI solutions. - Develop and implement **Generative AI** solutions using **Large Language Models (LLMs)** , **Retrieval-Augmented Generation (RAG)** , and prompt engineering techniques where applicable. - Perform model testing, validation, and continuous improvement to ensure reliability and scalability. - Document machine learning models, technical designs, and deployment processes. - Stay up to date with emerging AI, Machine Learning, and Databricks technologies. **Required Skills** - 2–4 years of experience in Machine Learning, AI, or Data Science. - Hands-on experience with the **Databricks Data Intelligence Platform** . - Strong programming skills in **Python** . - Experience with **PySpark** , **SQL** , and a working understanding of **Apache Spark** . - Experience developing machine learning models using **scikit-learn** , **TensorFlow** , or **PyTorch** . - Understanding of supervised and unsupervised learning techniques. - Knowledge of feature engineering, model evaluation, and model deployment. - Familiarity with **MLflow** and the machine learning lifecycle. - Good analytical, problem-solving, and communication skills. **Preferred Skills** - Experience with **Generative AI** , **LLMs** , **RAG** , AI agents, or prompt engineering. - Exposure to vector databases and embedding models. - Experience with cloud platforms such as **Microsoft Azure** , **AWS** , or **Google Cloud Platform** . - Basic understanding of MLOps and CI/CD for machine learning. - Familiarity with Azure AI services or cloud-based ML platforms. **Educational Qualification** - Bachelor's degree in Computer Science, Information Technology, Artificial Intelligence, Data Science, Engineering, or a related field. **Required Certifications** : - **Databricks Certified Machine Learning Associate** - **Databricks Certified Data Engineer Associate** **Preferred Certifications** : Candidates with one or more of the following certifications will be preferred: - **Microsoft Certified: Azure AI Engineer Associate (AI-102)** - **Microsoft Certified: Azure AI Fundamentals (AI-900)** - **Microsoft Certified: Azure Data Engineer Associate (DP-203)** **Key Competencies** - Strong analytical and problem-solving skills. - Passion for AI and machine learning innovation. - Effective communication and collaboration skills. - Ability to learn and adapt to new technologies. - Strong ownership, attention to detail, and commitment to delivering high-quality AI solutions. This description is tailored for a **2–4 year Machine Learning Engineer** who is expected to **build, train, and deploy AI models** while leveraging **Databricks** as the primary platform. It sets realistic expectations without requiring deep research or advanced MLOps expertise.