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5.0 - 9.0 years

0 Lacs

maharashtra

On-site

As a Senior Specialist in Software Development (Artificial Intelligence) at Accelya, you will lead the design, development, and implementation of AI and machine learning solutions to tackle complex business challenges. Your expertise in AI algorithms, model development, and software engineering best practices will be crucial in working with cross-functional teams to deliver intelligent systems that optimize business operations and decision-making. Your responsibilities will include designing and developing AI-driven applications and platforms using machine learning, deep learning, and NLP techniques. You will lead the implementation of advanced algorithms for supervised and unsupervised learning, reinforcement learning, and computer vision. Additionally, you will develop scalable AI models, integrate them into software applications, and build APIs and microservices for deployment in cloud environments or on-premise systems. Collaboration with data scientists and data engineers will be essential in gathering, preprocessing, and analyzing large datasets. You will also implement feature engineering techniques to enhance the accuracy and performance of machine learning models. Regular evaluation of AI models using performance metrics and fine-tuning them for optimal accuracy will be part of your role. Furthermore, you will collaborate with business stakeholders to identify AI adoption opportunities, provide technical leadership and mentorship to junior team members, and stay updated with the latest AI trends and research to introduce innovative techniques to the team. Ensuring ethical compliance, security, and continuous improvement of AI systems will also be key aspects of your role. You should hold a Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, or a related field, along with at least 5 years of experience in software development focusing on AI and machine learning. Proficiency in AI frameworks and libraries, programming languages such as Python, R, or Java, and cloud platforms for deploying AI models is required. Familiarity with Agile methodologies, data structures, and databases is essential. Preferred qualifications include a Master's or PhD in Artificial Intelligence or Machine Learning, experience with NLP techniques and computer vision technologies, and certifications in AI/ML or cloud platforms. Accelya is looking for individuals who are passionate about shaping the future of the air transport industry through innovative AI solutions. If you are ready to contribute your expertise and drive continuous improvement in AI systems, this role offers you the opportunity to make a significant impact in the industry.,

Posted 2 days ago

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13.0 - 20.0 years

45 - 65 Lacs

Hyderabad, Mumbai (All Areas)

Work from Office

Role & responsibilities Expertise in supervised, unsupervised, machine learning, deep learning, reinforcement learning, statistics techniques. Proficiency in Python, PyTorch, TensorFlow. Good to have knowledge of Bayesian inference, probability distribution, hypothesis testing, A/B testing, and time series forecasting. Hands-on experience with feature engineering and hyperparameter tuning. Experience with MLflow, Weights & Biases, DVC (Data Version Control). Ability to track model performance across multiple experiments and datasets. End-to-end ML lifecycle management from Data ingestion, preprocessing, feature engineering, model training, deployment, and monitoring. Expertise in CI/CD for ML, containerization (Docker, Kubernetes), and orchestration (Kubeflow). Good to have experience in automating data labelling and feature stores (Feast). Good to have data processing experience in Spark, Flink, Druid, Nifi. Good to have real-time data streaming in Kafka and other messaging systems Designing and optimizing data pipelines for structured data. Good to have hands-on experience with Pyspark. Strong SQL skills for data extraction, transformation, and query optimization. Atleast one database experience such as NOSQL database. Implementing parallel and distributed ML techniques for large-scale systems. Good to have knowledge of model explainability (SHAP, LIME), bias mitigation, and adversarial robustness. Experience in model drift monitoring. Good to have CUDA for GPU acceleration. Good to have choosing between CPU/GPU for different ML workloads (batch inference on CPU, training on GPU). Good to have scaling deep learning models on multi-GPU. Strong presentation skills, including data storytelling, visualization (Matplotlib, Seaborn, Superset), and report writing. Experience in mentoring data scientists, research associates, and data engineers. Contributions to research papers, patents, or open-source projects.

Posted 6 days ago

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