Principal Artificial Intelligence/Machine Learning Engineer (10-15 yrs)

Roles & Responsibilities :

- Develop machine learning , deep learning models and algorithms to solve complex business problems, improve processes, and enhance product functionality.

- Develop and deploy personalized large language models (LLMs).

- Develop document Parsing, NER, RAG, Chatbot and document systems.

- Build robust data and ML pipelines for production scale and performance.

- Optimize and fine-tune machine learning models for performance, scalability, and accuracy, leveraging techniques such as hyperparameter tuning and model optimization.

- Write robust, production-quality code using frameworks like PyTorch or TensorFlow.

- Stay updated on the latest advancements in AI/ML technologies, tools, and methodologies, incorporating best practices into development processes.

- Collaborate with stakeholders to understand business requirements, define project objectives, and deliver AI/ML solutions that meet customer needs and drive business value.

Requirements :

- Bachelor's or Master's Degree in Computer Science, Data Science, Computational Linguistics, Natural Processing (NLP), or other related fields.

- 10+ years of experience in developing and deploying machine learning models and algorithms, with hands-on experience with AI/ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn).

- Experience with Python, spacy, NLTK, knowledge graphs.

- Experience with search, particularly information retrieval, Elasticsearch, and relevance.

- Experience working with and fine-tuning existing models, especially from Hugging Face.

- Strong programming skills in languages such as Python, Java, or C++ for AI/ML model development and integration.

- Familiarity with web frameworks (FastAPI, Flask, Django, etc.) for building APIs.

- Knowledge of Agentic AI frameworks like LangChain, LangGraph, AutoGen, or Crew AI.

- Outstanding knowledge and experience in Data Science and MLOps in the fields of ML/DL, Generative AI with experience in containerization (Docker) and orchestration (Kubernetes) for deployment.

- Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and AI/ML deployment tools for scalable and reliable model deployment.

- Strong communication and collaboration skills, with the ability to work independently and collaboratively in a dynamic environment.