Job Description – Machine Learning Engineer

Position

Machine Learning Engineer

Experience

3–8 Years

Job Summary

We are looking for an experienced Machine Learning Engineer to design, develop, train, deploy, and maintain machine learning models and AI solutions. The ideal candidate should have strong expertise in Python, Machine Learning, Deep Learning, SQL, and MLOps, with experience working on end-to-end ML projects.

Key Responsibilities

- Design and develop machine learning models for business and technical use cases.

- Collect, clean, preprocess, and analyze large datasets.

- Perform feature engineering, feature selection, and exploratory data analysis.

- Develop, train, evaluate, and optimize ML models.

- Implement supervised and unsupervised machine learning algorithms.

- Apply appropriate statistical and machine learning techniques to solve business problems.

- Develop deep learning solutions using frameworks such as TensorFlow or PyTorch.

- Perform model validation, hyperparameter tuning, and performance optimization.

- Build reusable ML pipelines for model training and deployment.

- Deploy machine learning models into production environments.

- Monitor model performance, data quality, drift, and reliability.

- Collaborate with Data Scientists, Data Engineers, Software Engineers, and business stakeholders.

- Write clean, scalable, and maintainable Python code.

- Perform code reviews, testing, debugging, and documentation.

- Stay current with emerging AI/ML technologies and industry best practices.

Required Skills

- Strong hands-on experience with Python.

- Strong knowledge of Machine Learning algorithms and concepts.

- Experience with scikit-learn and other ML libraries.

- Experience with Pandas, NumPy, Matplotlib/Seaborn.

- Strong understanding of:

- Regression

- Classification

- Clustering

- Decision Trees

- Random Forest

- Gradient Boosting





- XGBoost/LightGBM

- Feature Engineering

- Model Evaluation

- Positive knowledge of statistics and probability.

- Strong SQL and database knowledge.

- Experience with model deployment and productionization.

- Understanding of MLOps and ML lifecycle management.

- Experience with Git/GitHub.

- Strong analytical and problem-solving skills.

Deep Learning – Good to Have

- Experience with TensorFlow, PyTorch, or Keras.

- Knowledge of CNNs, RNNs, LSTMs, and Transformers.

- Experience with Natural Language Processing (NLP).

- Experience with Computer Vision.

- Knowledge of Generative AI and Large Language Models (LLMs).

MLOps & Cloud – Good to Have

- Experience with MLflow, Kubeflow, or similar ML platforms.

- Knowledge of Docker and Kubernetes.

- Experience with CI/CD pipelines.

- Exposure to AWS, Azure, or Google Cloud.

- Knowledge of model serving and APIs.

- Experience with monitoring and model drift detection.

AI/GenAI Skills – Preferred

- Understanding of Generative AI and LLM architectures.

- Experience with Prompt Engineering and RAG.

- Familiarity with vector databases.

- Experience with frameworks such as LangChain or LlamaIndex.

- Knowledge of responsible AI, model security, and data privacy.

- Experience with GitHub Copilot or AI-assisted development tools.

Key Technologies

Python | Machine Learning | scikit-learn | Pandas | NumPy | SQL | TensorFlow | PyTorch | MLflow | Git | Docker | Kubernetes | AWS/Azure/GCP | GenAI | LLM | RAG

Education

Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Engineering, Mathematics, Statistics, or a related discipline.

Preferred Candidate Profile

The ideal candidate should have strong end-to-end Machine Learning development experience, from data preparation and model development through deployment and monitoring. Experience with MLOps, cloud platforms, Deep Learning, and Generative AI will be an added advantage.

📌 Machine Learning Engineer (Pune)
🏢 SPG Consulting
📍 Pune