AI/ML Engineer

Expertise :

-I and Machine Learning :

- Experience in Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision projects, products, and systems.

- Experience of working on ML algorithms, DL models (NLP based models). Vector Databases, Graph Databases etc.

- Generative AI Familiarity with Generative AI models including LLMs, RAG Approach, communication flow in both approaches, multimodal usage, open-source models from Hugging face, Lang chain frameworks, Vector DB (at least one DB should be well known in terms of indexing, storing the embeddings, extracting the answers), Prompts, Context based prompts etc.

- Experience with transformer-based and diffuser-based models (e.g., BERT, GPT, T5, Llama, Stable diffusion)

- Cloud Platforms : At-least experience of working on one of cloud platforms : AWS Sage maker, Azure OpenAI, Google Cloud Vertex AI, IBM WatsonX etc.

- Thorough knowledge on the various types of Models, F1 score, accuracy, training the models, tuning of models (with hyperparameters & all other techniques), possible statistical models and their fitment, model deployment, data pre/post processing.

- Knowledge should come out of their practical experience from their projects (no theoretical answers) either using cloud native services or open-source s/w.

- HW Platforms : At-least experience of working on Intel or Nvidia platforms (CPU+GPU)s

- Software Engineering agile development practices, requirements, design, architecture, debugging and validation of software products/projects

- Experience building commercial grade software, with much of that experience in a large scale, high availability service.

- Programming Experience Python, C, C++, C#, Java etc.

- Working knowledge of Distributed Systems and Cloud Technologies.

- Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch) to develop production-grade quality product

- Ability to debug, read code and work on a large and increasing codebase

- Good understanding of statistics, linear algebra, and probability theory

- Design and architecture : Ability to understand scalable, performant, secure, and efficient AI systems and solutions.

- Collaboration : Ability to work seamlessly with cross-functional teams, including data scientists, project engineers, delivery (customer), and practice teams.

- Communication : Ability to clearly convey technical concepts to both technical and non-technical audiences

- Problem solving analytical skills to address complex problems