Artificial Intelligence Architect

1 day ago

Rajkot, Gujarat, India beBeeAIEngineer Full time ₹ 15,00,000 - ₹ 20,00,000

We are seeking a skilled Generative AI Engineer to join our team at lt-technology-services.

Key Responsibilities:
  • LLM Integration: Develop, fine-tune, and integrate Large Language Models (LLMs) into enterprise applications, utilizing GPT, LLaMA, Claude, and other cutting-edge technologies.
  • Prompt Engineering: Implement prompt engineering techniques, embeddings, and Retrieval Augmented Generation (RAG) pipelines to enhance the performance of our AI solutions.
  • Agentic AI Development: Utilize Langgraph and other frameworks to build and maintain Agentic AI applications that can reason, learn, and interact with users in a more human-like manner.
  • API Development: Design, develop, and deploy APIs, microservices, and front-end/back-end integrations for GenAI applications, ensuring seamless integration with vector databases and cloud AI platforms.
  • Vector Database Optimization: Work with Pinecone, FAISS, Weaviate, Milvus, and other vector databases to enable semantic search and optimize model performance, latency, and scalability for real-world usage.
  • Cloud Deployment: Deploy solutions on cloud AI platforms, including Azure OpenAI, AWS Bedrock, and GCP Vertex AI, ensuring secure, scalable, and efficient deployment.
  • Collaboration and Quality Assurance: Collaborate with data scientists and solution architects to deliver Proof-of-Concepts (PoCs) and production-ready applications, adhering to responsible AI, data privacy, and security guidelines.
Required Skills:
  • Programming Skills: Strong programming skills in Python, with experience in APIs, microservices, Flask/FastAPI/Django.
  • Big Data Experience: Hands-on experience with Azure Databricks and Apache Spark, with a strong understanding of big data processing and analytics.
  • Vector Embeddings: Knowledge of vector embeddings and vector databases, including Pinecone, FAISS, Weaviate, and Milvus.
  • Orchestration Frameworks: Experience with LangChain, LlamaIndex, or similar orchestration frameworks for building and maintaining complex AI systems.
  • Docker and Kubernetes: Familiarity with Docker, Kubernetes, and MLOps practices for deploying AI models securely and efficiently.
  • Cloud AI Services: Experience working with cloud-based AI services, including Azure, AWS, and GCP, with a strong understanding of their respective ecosystems.

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