Machine Learning Engineer

SPG Consulting; Pune District; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

SPG Consulting seeks an experienced Machine Learning Engineer in Pune District, India, to design, develop, train, deploy, and maintain ML models and AI solutions. The role requires strong Python skills and deep knowledge of ML, DL, SQL, and MLOps, with hands-on experience on end-to-end ML projects.

The candidate will design and build ML models, perform data preprocessing and analysis, refine features, and deploy solutions into production, collaborating with data scientists, data engineers,

Qualifications

• Strong hands-on experience with Python and ML frameworks.
• Solid understanding of ML algorithms and concepts.
• Experience with ML libraries (scikit-learn, Pandas, NumPy).
• Experience with model deployment and productionization.
• Knowledge of MLOps and ML lifecycle management.

Responsibilities

• Design and develop machine learning models for business and technical use cases.
• Collect, clean, preprocess, and analyze large datasets.
• Develop, train, evaluate, and optimize ML models.
• Deploy machine learning models into production environments.
• Collaborate with data scientists, data engineers, and software engineers.
• Write clean, scalable, and maintainable Python code.

Skills

Python

Machine Learning

Deep Learning

SQL

MLOps

TensorFlow

PyTorch

scikit-learn

Pandas

NumPy

Git/GitHub

Education

Bachelor's or master's in CS/DS/AI/Engineering/Math/Statistics

Tools

Docker

Kubernetes

MLflow

Kubeflow

LangChain/LlamaIndex

Job description

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.

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

Decision Trees

XGBoost/LightGBM

Good 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

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.