We are seeking a highly analytical and technically proficient AI/ML Engineer to design, develop, and deploy intelligent algorithms that transform raw data into actionable insights. This role is a hybrid of data science and software engineering, requiring someone who can not only build sophisticated models but also ensure they are integrated into scalable, production-ready systems.

You will work across the entire machine learning lifecycle, from exploratory data analysis and feature engineering to model selection, deployment, and continuous monitoring.

Key Responsibilities

• Model Development: Design and train machine learning models (Supervised, Unsupervised, and Reinforcement Learning) to solve specific business challenges such as churn prediction, fraud detection, or demand forecasting.

• Data Pipeline Engineering: Collaborate with Data Engineers to build robust ETL pipelines that ensure high-quality data is available for model training and real-time inference.

• Algorithm Optimization: Fine-tune model hyperparameters and optimize algorithms for performance, accuracy, and computational efficiency.

• AI Integration: Deploy models as microservices via APIs to be consumed by web, mobile, or enterprise applications.

• Evaluation & Validation: Implement rigorous testing frameworks (A/B testing, cross-validation) to ensure model reliability and minimize bias or drift over time.

• Research & Innovation: Stay current with the latest advancements in AI, including Generative AI and Natural Language Processing (NLP), to identify new opportunities for innovation.

Requirements

• Education: Bachelor or Master degree in Computer Science, Data Science, Mathematics, or a related quantitative field.

• Experience: 2–5 years of professional experience in an AI/ML role, with a portfolio of models deployed in a production environment.

• Mathematical Foundation: Strong understanding of linear algebra, calculus, probability, and statistics.

• Programming: Expert proficiency in Python (specifically libraries like NumPy, Pandas, and Scikit-learn). Knowledge of C++ or R is an advantage.

Technical Stack

• Deep Learning Frameworks: PyTorch, TensorFlow, or Keras.

• NLP & GenAI: Experience with Hugging Face, LangChain, or OpenAI API.

• Big Data Tools: Spark, Hadoop, or SQL-based data warehouses (Snowflake/BigQuery).

• DevOps for ML: Docker, Kubernetes, and MLOps platforms like MLflow or Kubeflow.

• Cloud Platforms: AWS (SageMaker), Google Cloud (Vertex AI), or Azure Machine Learning.

Work Details

• Location:Pune

• Schedule: Full-time, Monday – Friday.

• Compensation: Competitive salary commensurate with experience + performance-based bonuses.

• Benefits: * Growth: Budget for continuous learning and AI certifications.

  • Health: Comprehensive medical and dental insurance.

  • Environment: High-spec hardware (MacBook Pro/NVIDIA GPUs) and access to cloud compute resources.