Description

You will design, train, and deploy LLMs and Generative AI models, ensuring they meet enterprise standards for latency, throughput, and cost efficiency.

Responsibilities

• Build and maintain Retrieval-Augmented Generation (RAG) pipelines to improve contextual accuracy and response quality.
• Convert AI models into scalable APIs and microservices using FastAPI, Flask, or Spring Boot.
• Optimize model performance for production environments, focusing on reliability and cost efficiency.
• Develop reusable ML code, data pipelines, and feature engineering frameworks for training and inference.
• Collaborate with data engineers and product teams to integrate AI solutions into enterprise workflows.

Required Skills

• 3+ years of hands-on experience developing and deploying machine learning models.
• 1+ year of focused experience with LLMs, Generative AI, or agentic AI solutions.
• Expert-level Python (3.8+) for model development and scripting.
• Proficient Java for enterprise system integration.
• Strong experience with Google Cloud Platform, specifically BigQuery and Vertex AI.
• Proven track record of deploying RAG architectures in production.
• Deep learning frameworks: TensorFlow, PyTorch, or Keras.
• Experience with MLOps tools like MLFlow for experiment tracking and model registry.
• Familiarity with SQL and NoSQL databases.

Preferred Skills

• Containerization and orchestration using Docker and Kubernetes.
• Vector databases such as FAISS, Pinecone, or Chroma.
• GCP or AI/ML certifications (e.g., Google Cloud Professional Data Engineer).

Key Skills

Education

Bachelor's or Master's degrees