What Jobs are available for Machine Learning Engineer in Delhi?

• Design, develop, and implement advanced machine learning models and algorithms for diverse applications.
• Lead the entire ML lifecycle, including data collection, cleaning, feature engineering, model training, validation, and deployment.
• Collaborate with data scientists, software engineers, and product managers to define ML requirements and translate them into robust solutions.
• Optimize model performance for accuracy, efficiency, and scalability.
• Stay current with state-of-the-art ML research and techniques, and proactively identify opportunities for application.
• Develop and maintain ML infrastructure, tools, and pipelines.
• Conduct thorough model evaluation and A/B testing to ensure effectiveness and impact.
• Mentor and guide junior machine learning engineers and data scientists.
• Communicate complex technical concepts and findings to both technical and non-technical audiences.
• Contribute to the company's intellectual property through patents and publications.

• Master's or Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a related quantitative field.
• Minimum of 7 years of experience in machine learning engineering or a closely related role.
• Proven track record of successfully developing and deploying ML models in production environments.
• Deep understanding of various ML algorithms (e.g., deep learning, reinforcement learning, natural language processing).
• Proficiency in programming languages such as Python and experience with ML frameworks like TensorFlow, PyTorch, or scikit-learn.
• Experience with big data technologies (e.g., Spark, Hadoop) and cloud platforms (AWS, GCP, Azure).
• Strong understanding of software engineering best practices, including version control, testing, and CI/CD.
• Excellent analytical, problem-solving, and critical thinking skills.
• Strong communication and leadership abilities.

• Design, implement, and deploy machine learning models and algorithms for various applications.
• Develop and maintain scalable ML pipelines for data preprocessing, feature engineering, model training, and inference.
• Collaborate with data scientists to validate models and ensure their performance meets business requirements.
• Work with software engineers to integrate ML models into existing systems and products.
• Optimize model performance for efficiency, accuracy, and scalability.
• Conduct experiments to evaluate different modeling approaches and hyperparameter tuning.
• Stay updated on the latest advancements in machine learning and AI technologies.
• Document ML models, processes, and codebase thoroughly.
• Troubleshoot and resolve issues related to ML model deployment and performance.
• Mentor junior engineers and contribute to the team's knowledge sharing.

• Bachelor's or Master's degree in Computer Science, Engineering, Statistics, or a related quantitative field.
• Minimum of 5 years of experience in machine learning engineering or a related role.
• Strong proficiency in programming languages such as Python, Java, or Scala.
• Hands-on experience with ML frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn, XGBoost).
• Solid understanding of data structures, algorithms, and software engineering best practices.
• Experience with cloud platforms (e.g., AWS, Azure, GCP) and MLOps practices.
• Knowledge of big data technologies (e.g., Spark, Hadoop) is a plus.
• Excellent problem-solving and analytical skills.
• Strong communication and collaboration abilities.
• Ability to work effectively in a hybrid team environment.

• Lead the design, development, and deployment of NLP models and systems.
• Mentor and guide a team of machine learning engineers.
• Conduct research into state-of-the-art NLP techniques and apply them to solve real-world problems.
• Optimize model performance, scalability, and efficiency.
• Collaborate with cross-functional teams to define project roadmaps and deliverables.
• Develop and maintain high-quality, production-ready code.
• Stay abreast of the latest advancements in AI and ML research.

• Master's or Ph.D. in Computer Science, Artificial Intelligence, or a related quantitative field.
• Proven experience (7+ years) in machine learning, with a strong focus on NLP.
• Proficiency in Python and ML libraries (TensorFlow, PyTorch, Scikit-learn).
• Experience with cloud platforms (AWS, Azure, GCP) and MLOps practices.
• Strong understanding of deep learning architectures relevant to NLP (e.g., Transformers, RNNs, LSTMs).
• Excellent problem-solving and analytical skills.
• Demonstrated leadership experience.

Job Title: H&E Image Analysis Scientist / Machine Learning Engineer- Spatial Omics (PhD)

Experience: Freshers

Location: Delhi

Job Description:

We are seeking a motivated PhD candidate interested in machine learning for histopathology

image analysis. The candidate will contribute to developing and optimizing deep learning

models to analyze digitized H&E slides for cancer classification and spatial mapping. This

role is well-suited for researchers aiming to apply advanced computational methods to

biomedical challenges.

Responsibilities:

● Design, develop, and train convolutional neural networks (CNNs) and related ML

models on H&E-stained histology images.

● Use and extend tools such as QuPath for cell annotations, segmentation models, and

dataset curation.

● Preprocess, annotate, and manage large image datasets to support model training

and validation.

● Collaborate with cross-disciplinary teams to integrate image-based predictions with

molecular and clinical data.

● Analyze model performance and contribute to improving accuracy, efficiency, and

robustness.

● Document research findings and contribute to publications in peer-reviewed journals.

Qualifications:

● PhD in Computer Science, Biomedical Engineering, Data Science, Computational

Biology, or a related discipline.

● Demonstrated research experience in machine learning, deep learning, or biomedical

image analysis (e.g., publications, thesis projects, or conference presentations).

● Strong programming skills in Python and experience with ML frameworks such as

TensorFlow or PyTorch.

● Familiarity with digital pathology workflows, image preprocessing/augmentation, and

annotation tools.

● Ability to work collaboratively in a multidisciplinary research environment.

Preferred:

● Background in cancer histopathology or biomedical image analysis.

● Knowledge of multimodal data integration, including spatial transcriptomics.

• Design, develop, and implement advanced machine learning models for computer vision tasks.
• Conduct research and experimentation to advance state-of-the-art in image and video analysis.
• Lead the end-to-end machine learning project lifecycle, from data to deployment.
• Optimize model performance, scalability, and efficiency.
• Collaborate with cross-functional teams to define project requirements and deliver solutions.
• Stay current with the latest research and industry trends in AI and Computer Vision.
• Mentor junior machine learning engineers and contribute to knowledge sharing.
• Develop and maintain ML pipelines and MLOps practices.
• Evaluate and integrate new technologies and tools.
• Present research findings and project progress to stakeholders.

• Ph.D. or Master's degree in Computer Science, AI, Machine Learning, or a related quantitative field.
• 5+ years of experience in machine learning, with a strong focus on Computer Vision.
• Proven experience with deep learning frameworks (e.g., TensorFlow, PyTorch).
• Expertise in algorithms like CNNs, RNNs, Transformers for vision.
• Strong programming skills in Python and C++.
• Experience with large-scale data processing and distributed training.
• Familiarity with cloud platforms (AWS, GCP, Azure) and MLOps.
• Excellent analytical and problem-solving abilities.
• Strong communication and teamwork skills.
• Demonstrated ability to lead projects and mentor team members.

• Assist in data cleaning, preprocessing, and feature engineering.
• Perform exploratory data analysis to uncover trends and insights.
• Support the development and testing of machine learning models.
• Utilize Python or R for data analysis and model building.
• Create data visualizations to communicate findings.
• Collaborate with senior data scientists on project tasks.
• Learn and apply data science methodologies and tools.
• Participate in code reviews and team discussions.
• Document analysis and model development processes.

• Bachelor's degree in a quantitative field (e.g., Statistics, Mathematics, Computer Science, Economics, Engineering) or equivalent practical experience.
• Basic understanding of statistics, probability, and linear algebra.
• Familiarity with programming concepts, preferably in Python or R.
• Enthusiasm for data science and machine learning.
• Strong analytical and problem-solving skills.
• Excellent communication and teamwork abilities.
• Eagerness to learn and adapt in a remote environment.
• Previous exposure to data analysis tools or projects is a plus.