Senior AI/ML Engineer / Lead (Clone)

Overall 5+ years of work experience as a Machine Learning Engineer or related role. Strong coding experience in Python is required. More than 4+ years of experience with designing and developing popular - highly scalable, distributed ML models and open-source projects. Contribution towards multiple use cases. Understanding of data structures, data modelling and software architecture. Familiarity and proficiency with machine learning frameworks (like PyTorch, TensorFlow, Keras, MxNet) and libraries (like scikit-learn, pandas). Knowledge of CUDA, OpenCL, Understanding and scripting on SQL/ NoSQL databases (e.g., MySQL, MangoDB) Familiarity with various operating systems (e.g., Windows, UNIX/Linux, Ubuntu) Knowledge in other programming languages like Java, .net, JavaScript is an added advantage. Knowledge on deployment of the ML models on any cloud-based platforms (AWS, Azure, GCP.,) Knowledge on Big Data & tools like Spark based - distributed training and inferencing. Reinforcement Learning, Neural Networks and Natural Language Processing knowledge on packages like NLTK, Gensim, SpaCy, CoreNLP is a bonus. Knowledge of text detection, generative models, reinforcement learning, model compression / optimization. Deep and profound knowledge of math, probability, statistics, and algorithms. Should have led a team of engineers towards deployment of projects. Outstanding analytical and problem-solving skills. Good team player and excellent written and verbal technical communication skills. Preferable to have FMCG/CPG domain knowledge. BE / BCA / BSc in Computer Science; Master’s degree in the same stream is a plus. Responsibilities: Deliver robust, well-tested, and fully documented modules to serve the use cases. Collaborate with Product Management and proactively provide inputs during new use case/feature/module development or connects with project teams. Act as an anchor to lead the developments from technical front. Design and work with engineering team to implement the entire application modules as discoverable microservices, experience hosting and deploying ML solutions. Collaborate with Product teams, system architects, designers, and engineers to support the development of robust machine learning systems. Continuously improve the efficiency, robustness, and cost of existing modules. Assess and recommend technology choices and directions in consideration of cost-benefit trade-offs. Build engineering plans, and schedule estimates. Perform code reviews and ensure proper design and delivery. Promote best practices and establishing team processes. Identify infrastructure and architectural investment needs. Hire, develop, and lead an inclusive, engaged, and high performing team