Designation: - Senior ML / MLOPs Engineer

Experience: Min 3.5 years

Location: - Noida (Sector- 132)

Key Responsibilities

- Model Development & Algorithm Optimization: Design, implement, and optimize ML models and algorithms using libraries and frameworks such as TensorFlow, PyTorch, and scikit-learn to solve complex business problems.
- Training & Evaluation: Train and evaluate models using historical data, ensuring accuracy,

scalability, and efficiency while fine-tuning hyperparameters.
- Data Preprocessing & Cleaning: Clean, preprocess, and transform raw data into a suitable format for model training and evaluation, applying industry best practices to ensure data quality.
- Feature Engineering: Conduct feature engineering to extract meaningful features from data that enhance model performance and improve predictive capabilities.
- Model Deployment & Pipelines: Build end-to-end pipelines and workflows for deploying machine learning models into production environments, leveraging Azure Machine

Learning and containerization technologies like Docker and Kubernetes.
- Production Deployment: Develop and deploy machine learning models to production environments, ensuring scalability and reliability using tools such as Azure Kubernetes

Service (AKS).
- End-to-End ML Lifecycle Automation: Automate the end-to-end machine learning lifecycle, including data ingestion, model training, deployment, and monitoring, ensuring seamless operations and faster model iteration.
- Performance Optimization: Monitor and improve inference speed and latency to meet real time processing requirements, ensuring productive and scalable solutions.
- NLP, CV, GenAI Programming: Work on machine learning projects involving Natural

Language Processing (NLP), Computer Vision (CV), and Generative AI (GenAI),

applying state-of-the-art techniques and frameworks to improve model performance.

- Collaboration & CI/CD Integration:



Collaborate with data scientists and engineers to integrate ML models into production workflows, building and maintaining continuous integration/continuous deployment (CI/CD) pipelines using tools like Azure DevOps, Git,

and Jenkins.
- Monitoring & Optimization: Continuously monitor the performance of deployed models,

adjusting parameters and optimizing algorithms to improve accuracy and efficiency.
- Security & Compliance: Ensure all machine learning models and processes adhere to industry security standards and compliance protocols, such as GDPR and HIPAA.
- Documentation & Reporting: Document machine learning processes, models, and results to ensure reproducibility and effective communication with stakeholders.

Required Qualifications

- Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.
- Min 3.5 years+ of experience in machine learning operations (MLOps), cloud engineering, or similar roles.
- Proficiency in Python, with hands-on experience using libraries such as TensorFlow,

PyTorch, scikit-learn, Pandas, and NumPy.
- Strong experience with Azure Machine Learning services, including Azure ML Studio,

Azure Databricks, and Azure Kubernetes Service (AKS).
- Knowledge and experience in building end-to-end ML pipelines, deploying models, and automating the machine learning lifecycle.
- Expertise in Docker, Kubernetes, and container orchestration for deploying machine learning models at scale.
- Experience in data engineering practices and familiarity with cloud storage solutions like

Azure Blob Storage and Azure Data Lake.
- Strong understanding of NLP, CV, or GenAI programming, along with the ability to apply these techniques to real-world business problems.
- Experience with Git, Azure DevOps, or similar tools to manage version control and CI/CD pipelines.
- Solid experience in machine learning algorithms, model training, evaluation, and hyperparameter tuning

📌 Senior Machine Learning Engineer (Noida)
🏢 ThoughtSol Infotech
📍 Noida