Embark on a journey of discovery and innovation as an AI/ML Engineer. This role offers the opportunity to become a subject matter expert while actively contributing to team discussions and developing thoughtful solutions for complex challenges. You will engage deeply with advanced Machine Learning, driving growth and shaping the future.

Join us to explore new horizons with enthusiasm and curiosity. This position invites you to leverage your expertise and passion to foster innovation and contribute to groundbreaking AI advancements.

Design and implement cutting-edge AI/ML and Generative AI solutions using Python. Build and deploy applications that harness the power of Large Language Models (LLMs). Develop comprehensive end-to-end pipelines, from data ingestion and preprocessing to model integration and deployment. Apply prompt engineering and fine-tuning strategies specifically for LLMs. Integrate AI services seamlessly into web, mobile, and backend applications through robust APIs. Utilize vector databases and embeddings to enhance semantic search and RAG architectures. Optimize models and pipelines for peak performance, reduced latency, and cost efficiency. Collaborate with cross-functional teams to translate business requirements into actionable AI strategies. Champion responsible AI practices, ensuring data privacy, bias mitigation, and explainability. Continuously monitor and enhance model performance within production environments.

A strong foundation in software engineering or AI/ML, with 6-8 years of hands-on experience. Expertise in Python programming is essential for this role. Demonstrated experience with popular ML/DL frameworks such as PyTorch or TensorFlow. Proficiency with LLM frameworks like LangChain or LlamaIndex. A solid grasp of Natural Language Processing (NLP) concepts and transformer-based models. Familiarity with REST APIs and microservices architecture. Experience working with vector databases (e.g., Pinecone, FAISS, Weaviate). Proficiency in using version control systems, particularly Git. Candidates with experience using APIs from providers like OpenAI or open-source LLMs (Llama, Mistral) are highly desirable. Knowledge of Retrieval-Augmented Generation (RAG) architectures and model fine-tuning/evaluation techniques is preferred. Exposure to cloud platforms like AWS or GCP for deployment is a plus. Familiarity with containerization tools such as Docker is also advantageous. Strong analytical and problem-solving abilities are key. Capacity to translate intricate AI concepts into practical, implementable solutions. Excellent communication and stakeholder management skills are required. Ability to thrive both independently and collaboratively within a team setting.