Machine Learning Engineer Job Vacancies in Delhi

***Responsibilities:***1. Contribute to the development of software and solutions, emphasizing ML/NLP as a key component, to productize research goals and deployable services.2. Collaborate closely with the frontend team and research team to integrate machine learning models into deployable services.3. Utilize and develop state-of-the-art algorithms and models for NLP/ML, ensuring they align with the product and research objectives.4. Perform thorough analysis to improve existing models, ensuring their efficiency and effectiveness in real-world applications.5. Engage in data engineering tasks to clean, validate, and preprocess data for uniformity and accuracy, supporting the development of robust ML models.6. Stay abreast of new developments in research and engineering in NLP and related fields, incorporating relevant advancements into the product development process.7. Actively participate in agile development methodologies within dynamic research and engineering teams, adapting to evolving project requirements.8. Collaborate effectively within cross-functional teams, fostering open communication and cooperation between research, development, and frontend teams.9. Actively contribute to building an open, transparent, and collaborative engineering culture within the organization.10. Demonstrate strong software engineering skills to ensure the reliability, scalability, and maintainability of deployable ML services.11. Take ownership of the end-to-end deployment process, including the deployment of ML models to production environments.12. Work on continuous improvement of deployment processes and contribute to building a seamless pipeline for deploying and monitoring ML models in real-world applications.

***Responsibilities:***1. Contribute to the development of software and solutions, emphasizing ML/NLP as a key component, to productize research goals and deployable services.2. Collaborate closely with the frontend team and research team to integrate machine learning models into deployable services.3. Utilize and develop state-of-the-art algorithms and models for NLP/ML, ensuring they align with the product and research objectives.4. Perform thorough analysis to improve existing models, ensuring their efficiency and effectiveness in real-world applications.5. Engage in data engineering tasks to clean, validate, and preprocess data for uniformity and accuracy, supporting the development of robust ML models.6. Stay abreast of new developments in research and engineering in NLP and related fields, incorporating relevant advancements into the product development process.7. Actively participate in agile development methodologies within dynamic research and engineering teams, adapting to evolving project requirements.8. Collaborate effectively within cross-functional teams, fostering open communication and cooperation between research, development, and frontend teams.9. Actively contribute to building an open, transparent, and collaborative engineering culture within the organization.10. Demonstrate strong software engineering skills to ensure the reliability, scalability, and maintainability of deployable ML services.11. Take ownership of the end-to-end deployment process, including the deployment of ML models to production environments.12. Work on continuous improvement of deployment processes and contribute to building a seamless pipeline for deploying and monitoring ML models in real-world applications.

We are seeking a skilled MLOps Engineer to lead the automation of the ML lifecycle from training to deployment. You will manage scalable CI/CD pipelines, containerized workflows, and performance monitoring across multiple deployment targets.

We are hiring an experienced ML Engineer to lead the design and deployment of robust, production-ready AI systems. You will drive experimentation, model validation, and edge performance benchmarking for scalable biometric applications.

Level of skills and experience:5 years of hands-on experience in using Python, Spark,Sql.Experienced in AWS Cloud usage and management.Experience with Databricks (Lakehouse, ML, Unity Catalog, MLflow).Experience using various ML models and frameworks such as XGBoost, Lightgbm, Torch.Experience with orchestrators such as Airflow and Kubeflow.Familiarity with containerization and orchestration technologies (e.g., Docker, Kubernetes).Fundamental understanding of Parquet, Delta Lake and other data file formats.Proficiency on an IaC tool such as Terraform, CDK or Cloud Formation.Strong written and verbal English communication skill and proficient in communication with non-technical stakeholderst

Position: ML EngineerQualification: Bachelor's Degree Experience: 2+ years of experience in developing large scale ML modelsLocation: Work from HomeRoles & responsibilities: You will identify, leverage, and successfully evangelize opportunities to improve AI productivity You will prototype, develop and experiment with computer vision techniques and explore a variety of approaches You will understand product requirements and translate them into engineering goals and tasks You will explore new methods to leverage a large data-set of spontaneous real-world visual & image data You will build systems for an end to end production-grade stack with low latency and high throughput You will design, implement and optimize algorithms to run on edge devices You will leverage the Edge compute available to run a variety of offline algorithms spawning multiple use casesQualifications: Bachelors degree in Computer Science or related technical field or equivalent technical experience 2+ years of experience in developing large scale machine learning models Exposure in deep learning techniques (CNN, RNN, LSTM) on computer vision tasks (object detection, classification, object tracking) Expertise in the machine learning libraries such as Sci-Kit, Numpy, Pandas, OpenCV Experience working with deep learning frameworks (e.g. TensorFlow, Keras, PyTorch) including implementing custom layers Experience in working on large scale data, and deploying ML solutions into production environments Experience with Docker, and/or cloud deployment technologies Strong expertise in C++, and Python programming languages Strong expertise in Bash, PowerShell Strong expertise in working on Unix/Linux environments Experience with SQL, NoSQL databases Strong interpersonal, written, and verbal communication skillsAdditional: Experience with Ansible, Kubernetes, Docker etc. and/or cloud deploy deployment technologies Experience with Go programming language

Production Machine Learning Models with Django API Designs Machine Learning Model Deployment using Django Strong hands-on experience in Python Experience on any one of the Cloud platforms AWS/ Azure/ GCP Experience building custom integrations between cloud-based systems using APIs Excellent communication (verbal and written) skills can communicate complex ideas in simple ways Experience developing and maintaining ML systems built with open source tools Experience developing with containers (Docker) in cloud computing environments is a plus Exposure to machine learning methodology and best practices