22 Computer jobs in Neyyattinkara

• Develop, train, and deploy deep learning models for various computer vision tasks such as object detection, image segmentation, and facial recognition.
• Implement and optimize computer vision algorithms using frameworks like TensorFlow, PyTorch, and OpenCV.
• Process and analyze large image and video datasets to extract meaningful features and insights.
• Collaborate with product managers and other engineers to define project requirements and develop solutions.
• Ensure the scalability, efficiency, and robustness of deployed computer vision models.
• Stay current with the latest research and advancements in computer vision and deep learning.
• Write clean, well-documented, and maintainable code.
• Conduct rigorous testing and validation of AI models.
• Contribute to the continuous improvement of our AI development pipelines and infrastructure.
• Participate in code reviews and knowledge-sharing sessions with the team.

• Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related field with a focus on AI/ML.
• Proven experience in developing and deploying computer vision models.
• Proficiency in Python and relevant libraries (e.g., TensorFlow, PyTorch, Keras, OpenCV).
• Strong understanding of deep learning concepts and architectures relevant to computer vision.
• Experience with data augmentation techniques and handling large datasets.
• Familiarity with cloud platforms (AWS, Azure, GCP) for model training and deployment.
• Excellent problem-solving skills and attention to detail.
• Strong communication skills and ability to work effectively in a remote, collaborative team environment.
• Experience with MLOps practices is a plus.

About Innodata:

Innodata (NASDAQ: INOD) is a leading data engineering company serving over 2,000 customers worldwide. We are the AI solutions provider-of-choice for four of the five largest global technology companies, as well as top-tier organizations in finance, insurance, law, healthcare, and more.

With a global workforce of over 5,000 employees and presence in 13 cities across the US, Canada, UK, Germany, Israel, India, Sri Lanka, and the Philippines, we combine advanced ML/AI technologies, subject matter expertise, and secure infrastructure to unlock the full potential of artificial intelligence.

About the Role:

We are seeking highly analytical and detail-oriented scientific experts to support our AI training and model evaluation initiatives. This role is ideal for PhDs in Physics, Chemistry, Biology, or Computer Science who have a passion for research, critical thinking, and applying domain-specific knowledge to cutting-edge AI applications.

You will contribute to the development and improvement of AI systems, including large language models (LLMs) and other machine learning pipelines, by creating, curating, and evaluating scientific datasets, validating model outputs, and providing domain-specific insights.

Key Responsibilities:

• Create, review, or annotate high-quality scientific content and datasets to train or evaluate AI systems.
• Perform quality assurance on model-generated outputs for scientific accuracy, clarity, and alignment with domain knowledge.
• Analyze and interpret AI behavior in the context of domain-specific tasks and error patterns.
• Support the development of guidelines for scientific content generation and annotation.
• Collaborate with internal engineering, data, and linguistic teams to ensure accuracy and consistency across projects.
• Conduct domain-specific research and synthesize findings to guide model improvements.
• Identify and resolve issues related to ambiguity, bias, or misrepresentation in scientific content.

Qualifications:

• PhD in Computer Science.
• Strong analytical skills and ability to apply theoretical knowledge to real-world datasets and AI systems.
• Familiarity with scientific writing standards, peer-reviewed publishing, or lab-based research methodology.
• Attention to detail and ability to critically evaluate scientific content for accuracy and clarity.
• Excellent writing, editing, and communication skills.
• (Preferred) Experience with AI/ML concepts, data annotation, programming, or computational modeling.

Nice to Have:

• Experience working with large datasets or scientific databases.
• Knowledge of machine learning pipelines, NLP, or LLMs.
• Previous experience in interdisciplinary research or technical consulting.

About Innodata:

Innodata (NASDAQ: INOD) is a leading data engineering company serving over 2,000 customers worldwide. We are the AI solutions provider-of-choice for four of the five largest global technology companies, as well as top-tier organizations in finance, insurance, law, healthcare, and more.

With a global workforce of over 5,000 employees and presence in 13 cities across the US, Canada, UK, Germany, Israel, India, Sri Lanka, and the Philippines, we combine advanced ML/AI technologies, subject matter expertise, and secure infrastructure to unlock the full potential of artificial intelligence.

About the Role:

We are seeking highly analytical and detail-oriented scientific experts to support our AI training and model evaluation initiatives. This role is ideal for PhDs in Physics, Chemistry, Biology, or Computer Science who have a passion for research, critical thinking, and applying domain-specific knowledge to cutting-edge AI applications.

You will contribute to the development and improvement of AI systems, including large language models (LLMs) and other machine learning pipelines, by creating, curating, and evaluating scientific datasets, validating model outputs, and providing domain-specific insights.

Key Responsibilities:

• Create, review, or annotate high-quality scientific content and datasets to train or evaluate AI systems.
• Perform quality assurance on model-generated outputs for scientific accuracy, clarity, and alignment with domain knowledge.
• Analyze and interpret AI behavior in the context of domain-specific tasks and error patterns.
• Support the development of guidelines for scientific content generation and annotation.
• Collaborate with internal engineering, data, and linguistic teams to ensure accuracy and consistency across projects.
• Conduct domain-specific research and synthesize findings to guide model improvements.
• Identify and resolve issues related to ambiguity, bias, or misrepresentation in scientific content.

Qualifications:

• PhD in Computer Science.
• Strong analytical skills and ability to apply theoretical knowledge to real-world datasets and AI systems.
• Familiarity with scientific writing standards, peer-reviewed publishing, or lab-based research methodology.
• Attention to detail and ability to critically evaluate scientific content for accuracy and clarity.
• Excellent writing, editing, and communication skills.
• (Preferred) Experience with AI/ML concepts, data annotation, programming, or computational modeling.

Nice to Have:

• Experience working with large datasets or scientific databases.
• Knowledge of machine learning pipelines, NLP, or LLMs.
• Previous experience in interdisciplinary research or technical consulting.

• Conduct cutting-edge research in computer vision and deep learning.
• Develop and implement novel algorithms for image analysis and understanding.
• Design, train, and evaluate deep neural networks for various computer vision tasks.
• Collaborate with cross-functional teams to integrate AI models into products.
• Publish research findings in leading academic conferences and journals.
• Stay abreast of the latest advancements in AI, machine learning, and computer vision.
• Develop prototypes and proof-of-concepts for new AI applications.
• Optimize models for performance, efficiency, and deployment.
• Mentor junior researchers and contribute to the team's knowledge base.
• Contribute to intellectual property generation through patents and publications.

• Ph.D. or Master's degree in Computer Science, AI, Machine Learning, or a related quantitative field.
• Proven research experience and publications in computer vision conferences (e.g., CVPR, ICCV, ECCV).
• Strong theoretical understanding of machine learning and deep learning.
• Expertise in deep learning frameworks (TensorFlow, PyTorch).
• Proficiency in Python and relevant libraries (NumPy, SciPy, OpenCV).
• Experience with large-scale datasets and cloud computing platforms.
• Strong analytical, problem-solving, and critical thinking skills.
• Excellent communication and collaboration skills for a remote environment.
• Ability to work independently and manage research projects effectively.