Location : Hyderabad

Employment Type : Full-Time

Experience Level : 4-7 years

Notice : 30 days

Mode : 4 days WFO

Job Description :

Responsibilities :

- Design, build, and deploy ML models : Develop robust, scalable machine learning models that can be integrated into production environments.

- Data Preprocessing and Feature Engineering : Extract, clean, and prepare large datasets from diverse sources to create high-quality input features for machine learning models.

- Model Training and Tuning : Implement and experiment with various machine learning algorithms (supervised, unsupervised, reinforcement learning) and optimize hyperparameters to improve performance and efficiency.

- Model Evaluation : Develop evaluation metrics, assess model performance, and refine models to ensure they meet desired accuracy and precision levels.

- ML Pipeline Automation : Design and maintain machine learning pipelines to automate model training, validation, deployment, and monitoring, ensuring reproducibility and scalability.

- Collaborate across teams : Work closely with cross-functional teams such as data engineering, product management, and software development to deliver integrated AI-driven solutions.

- Production Deployment : Optimize model performance for production environments by managing memory, computing power, and inference time.

- Model Monitoring and Maintenance : Set up real-time monitoring systems to track model performance post-deployment and make necessary adjustments or re-train models as needed.

- Stay Updated : Continuously research the latest advancements in machine learning algorithms, tools, and platforms and evaluate their applicability to current projects.

- Mentorship and Guidance : Share expertise with junior engineers and contribute to a collaborative team environment.

Technical Skills :

- Algorithms : Solid understanding of machine learning algorithms (classification, regression, clustering, reinforcement learning) and deep learning techniques (CNNs, RNNs, GANs).

- Programming Languages : Expertise in Python, with experience in other languages such as NodeJs, R, C++ being a plus.

- ML Frameworks : Proficiency in using machine learning libraries and frameworks such as TensorFlow, PyTorch, Scikit-learn, Keras, XGBoost, LightGBM, etc.

- Data Management : Strong experience working with large databases (SQL, NoSQL), big data platforms (Hadoop, Spark), and cloud services (AWS or GCP or Azure).

- Data Visualization : Experience with data visualization tools (Matplotlib, Seaborn, Tableau) to communicate model insights and results.

- Version Control and MLOps : Familiarity with MLOps practices, version control (Git), and CI/CD pipelines for ML model deployment.

- Cloud and GPU Acceleration : Experience in deploying models on cloud platforms and optimizing for hardware accelerators like GPUs/TPUs.

Experience :

- 4+ years of hands-on experience in machine learning model development and deployment.

- Experience in end-to-end machine learning projects from research and prototyping to production deployment.

- Experience working with large-scale datasets and optimizing models for performance in real-time systems.

- Proven ability to work on cross-functional teams and collaborate with stakeholders to solve business problems using ML.

Preferred Qualifications :

- Advanced degree (Masters/PhD) in Computer Science, Machine Learning, Data Science, or a related field.

- Experience with natural language processing (NLP), computer vision, or reinforcement learning is a strong plus.

- Contributions to open-source projects in the ML community.

- Knowledge of DevOps practices in machine learning, including model monitoring and feedback loops.