Senior AI/ML Engineer

Position: Lead AI/ML Engineer

Qualifications:

• 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; Masters 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.