Responsibilities:

Develop and implement machine learning models and LLMs, optimizing them for real-world deployment
Fine-tune and optimize large language models (LLMs) to enhance performance and scalability
Work with AI development frameworks like LangChain, PyTorch, Keras, etc. for AI-driven applications
Design and develop machine learning algorithms and deep learning applications and systems
Solve complex problems with multilayered data sets, and optimize existing machine learning libraries and frameworks
Train and monitor machine learning systems and models as needed
Design and implement embedding-based similarity algorithms to improve AI-powered search and retrieval
Implement Retrieval-Augmented Generation (RAG) techniques for enhanced AI response generation
Utilize vector databases (e.g. Chroma, Pinecone, Weaviate, etc.) to support efficient data retrieval
Develop and deploy AI/ML pipelines
Work with frameworks like FastAPI, Flask, or Django to integrate AI models into applications
Collaborate with Data Engineers to integrate AI models with Vector databases and conventional Databases
Optimize AI inference for low latency and high scalability in production environments
Implement AI security best practices to mitigate risks like adversarial attacks
Stay up to date with advancements in LLMs, ML, AI search, and model deployment strategies

Skills and Requirements:

Bachelor's or master's degree in computer science, Data Science, Engineering, or a related field
Strong proficiency in Python for AI/ML development
Hands-on experience with LLMs and transformer-based architectures
Hands-on experience with ML based development
Experience implementing RAG techniques in AI applications
Familiarity with embedding models and NLP pipelines
Understanding of AI inference optimization and scalability strategies
Experience with MLOps tools is a plus
Knowledge of containerization and deployment using Docker, Kubernetes, or cloud platforms (AWS, Azure)

Offerings & Benefits:

AI/ML Engineer