3 Ml Workflow Jobs

We are Hiring Senior Python with Machine Learning Engineer Level 3 for a US based IT Company based in Hyderabad. Candidates with minimum 7 Years of experience in python and machine learning can apply. Job Title : Senior Python with Machine Learning Engineer Level 3 Location : Hyderabad Experience : 7+ Years CTC : 28 LPA - 30 LPA Working shift : Day shift Job Description: We are seeking a highly skilled and experienced Python Developer with a strong background in Machine Learning (ML) to join our advanced analytics team. In this Level 3 role, you will be responsible for designing, building, and deploying robust ML pipelines and solutions across real-time, batch, event-driven, and edge computing environments. The ideal candidate will have extensive hands-on experience in developing and deploying ML workflows using AWS SageMaker , building scalable APIs, and integrating ML models into production systems. This role also requires a strong grasp of the complete ML lifecycle and DevOps practices specific to ML projects. Key Responsibilities: Develop and deploy end-to-end ML pipelines for real-time, batch, event-triggered, and edge environments using Python Utilize AWS SageMaker to build, train, deploy, and monitor ML models using SageMaker Pipelines, MLflow, and Feature Store Build and maintain RESTful APIs for ML model serving using FastAPI , Flask , or Django Work with popular ML frameworks and tools such as scikit-learn , PyTorch , XGBoost , LightGBM , and MLflow Ensure best practices across the ML lifecycle: data preprocessing, model training, validation, deployment, and monitoring Implement CI/CD pipelines tailored for ML workflows using tools like Bitbucket , Jenkins , Nexus , and AUTOSYS Design and maintain ETL workflows for ML pipelines using PySpark , Kafka , AWS EMR , and serverless architectures Collaborate with cross-functional teams to align ML solutions with business objectives and deliver impactful results Required Skills & Experience: 5+ years of hands-on experience with Python for scripting and ML workflow development 4+ years of experience with AWS SageMaker for deploying ML models and pipelines 3+ years of API development experience using FastAPI , Flask , or Django 3+ years of experience with ML tools such as scikit-learn , PyTorch , XGBoost , LightGBM , and MLflow Strong understanding of the complete ML lifecycle: from model development to production monitoring Experience implementing CI/CD for ML using Bitbucket , Jenkins , Nexus , and AUTOSYS Proficient in building ETL processes for ML workflows using PySpark , Kafka , and AWS EMR Nice to Have: Experience with H2O.ai for advanced machine learning capabilities Familiarity with containerization using Docker and orchestration using Kubernetes For further assistance contact/whatsapp : 9354909517 or write to hema@gist.org.in

Strong proficiency in Python for ML workflows (must-have); familiarity with Java/Scala is a plus. Experience building production-grade ML models for real-time or low-latency systems. Hands-on with scikit-learn, XGBoost, and optionally PyTorch or TensorFlow. Familiar with MLFlow, Airflow, and deploying models using APIs or inference services. Comfortable working in GCP, AWS, or Azure environments. Builds production-ready ML models for bid shaping, audience prediction, and adaptive optimization. Build real-time ML models for bid shaping using OpenRTB signals and auction dynamics. Implement adaptive bidding models that optimize toward a target win rate (e.g., 15%). Develop reinforcement learning or control systems for dynamic pricing and margin adjustment. Collaborate with data scientists to productionize insights into bidding models. Build scalable inference pipelines that operate within latency budgets for real-time bidding.

Role Description As an AI/ML Engineer at MySGS & Co., you will play a key role in designing, developing, and maintaining machine learning (ML) systems and AI-driven automation solutions. You will collaborate closely with product managers, data scientists, data engineers, architects, and other cross-functional teams to build scalable and production-ready ML models. Your role will focus on both ML development and MLOps, ensuring seamless deployment, monitoring, and automation of ML models in real-world applications. You will be responsible for optimizing end-to-end ML pipelines, automating workflows, managing model lifecycle operations (MLOps), and ensuring AI systems are scalable and cost-efficient. You will embrace a build-measure-learn approach, continuously iterating and improving models for performance and reliability. Responsibilities Design, develop, and maintain ML models to solve business challenges and drive automation. Implement and optimize ML algorithms for efficiency, scalability, and AI-powered insights. Conduct experiments, A/B testing, and model evaluations to improve performance. Develop, containerize, and deploy AI/ML systems in production environments using best practices. Automate and streamline ML pipelines, ensuring smooth transitions from development to production. Monitor and troubleshoot the performance, accuracy, and drift of ML models in production. Execute and automate model validation tests, ensuring robustness and reliability. Optimize training and inference workflows, enhancing model efficiency and speed. Manage model versioning, deployment strategies, and rollback mechanisms. Implement and maintain CI/CD pipelines for ML models, ensuring smooth integration with engineering workflows. Review code changes, pull requests, and pipeline configurations to uphold quality standards. Stay updated with emerging AI/ML technologies, MLOps best practices, and cloud-based ML platforms. Skills and Qualifications Strong programming skills in Python or R, with experience in ML frameworks (TensorFlow, PyTorch, Scikit-learn) Experience deploying and maintaining ML models using Docker, Kubernetes, and cloud-based AI services (AWS Sagemaker, GCP Vertex AI, Azure ML). Solid understanding of MLOps principles, including CI/CD for ML models, model monitoring, and automated retraining. Knowledge of data engineering principles, data preprocessing, and feature engineering for ML pipelines. Familiarity with workflow orchestration tools. Experience with real-time model serving and API deployment. Strong analytical and problem-solving skills with a keen attention to detail. Ability to collaborate cross-functionally and work in a fast-paced AI-driven