Strong working experience in machine learning, with a proven track record of delivering impactful solutions in NLP, machine vision, and AI.
Must have experience in AI/ML architecture design and implementation in data / big data using cloud infrastructure.
Proficiency in programming languages such as Python or R, and experience with data manipulation libraries (e.g., Pandas, NumPy).
Strong understanding of statistical concepts and techniques, and experience applying them to real-world problems.
Strong programming skills in Python, and proficiency in deep learning frameworks such as TensorFlow, PyTorch, or JAX, as well as machine learning libraries such as scikit-learn.
Should have experience in SQL.
Should have understanding of MLOps and at least one deployment using some of the following technologies: MLflow, Kubeflow, Docker, Kubernetes, model deployment pipelines.
Should have designed, developed, and deployed a few AI agents as part of multi-agent systems for autonomous/semi-autonomous decision-making and agent orchestration.
Strong understanding of LLMs and foundation models with an expertise in designing and building prompts for prompt development and templates.
Practical experience with Generative AI frameworks such as GANs, VAEs, prompt engineering, and retrieval-augmented generation (RAG), and the ability to apply them to real-world problems.
Excellent problem-solving skills, with a creative and analytical mindset.
Strong communication and teamwork skills, with the ability to work effectively in a team environment and interact with stakeholders at all levels.
Experience with AI ethics and responsible AI practices
RESPONSIBILITIES:
Understanding the client鈥檚 business use cases and technical requirements and be able to convert them into technical design which elegantly meets the requirements.
Mapping decisions with requirements and be able to translate the same to developers.
Identifying different solutions and being able to narrow down the best option that meets the client鈥檚 requirements.
Defining guidelines and benchmarks for NFR considerations during project implementation
Writing and reviewing design document explaining overall architecture, framework, and high-level design of the application for the developers
Reviewing architecture and design on various aspects like extensibility, scalability, security, design patterns, user experience, NFRs, etc., and ensure that all relevant best practices are followed.
Developing and designing the overall solution for defined functional and non-functional requirements; and defining technologies, patterns, and frameworks to materialize it
Understanding and relating technology integration scenarios and applying these learnings in projects
Resolving issues that are raised during code/review, through exhaustive systematic analysis of the root cause, and being able to justify the decision taken.
Carrying out POCs to make sure that suggested design/technologies meet the requirements.