Python GenAI Engineer

Role Overview: You will be a Senior Generative AI / Backend Engineer responsible for developing AI-powered applications and scalable backend systems. Your expertise in LLMs, RAG, prompt engineering, and Python-based backend development will be crucial in delivering production-ready AI solutions. Key Responsibilities: - Design, develop, and deploy generative AI solutions using frameworks like LangChain and LlamaIndex. - Utilize advanced prompt engineering techniques to optimize LLM responses and work with advanced LLM features. - Implement RAG workflows by integrating vector databases such as Pinecone, Weaviate, Supabase, or PGVector. - Develop solutions using embeddings and similarity search for intelligent query resolution. - Process and analyze multimodal data for AI-driven applications. - Integrate observability and monitoring tools to track LLM performance. Backend Engineering: - Build and maintain scalable, secure backend services using Python frameworks like FastAPI, Django, or Flask. - Design and implement RESTful APIs for system integration. - Optimize and manage relational and vector databases for AI workflows. - Implement asynchronous programming patterns and follow clean coding practices. - Integrate third-party SDKs and APIs for system interoperability. - Develop backend pipelines for multimodal data processing. - Manage background tasks using Celery, cron jobs, or job queues. - Utilize Docker for reproducible deployments. - Ensure backend systems are secure and scalable for production. Qualifications: Essential Skills: - Proficiency in Python and backend frameworks. - Hands-on experience with Generative AI frameworks and RAG architectures. - Understanding of LLMs, embeddings, and similarity search techniques. - Experience with relational and vector databases. - Deployment of AI solutions using Docker. - Skills in testing, debugging, and performance optimization. - Understanding of asynchronous programming. Preferred Skills: - Experience with cloud platforms like AWS, GCP, or Azure. - Basic knowledge of frontend technologies. - Experience with observability and monitoring tools. - Awareness of emerging trends in generative AI and multimodal AI. - Knowledge of secure coding practices. - Relevant certifications in AI, Machine Learning, or Cloud technologies. Role Overview: You will be a Senior Generative AI / Backend Engineer responsible for developing AI-powered applications and scalable backend systems. Your expertise in LLMs, RAG, prompt engineering, and Python-based backend development will be crucial in delivering production-ready AI solutions. Key Responsibilities: - Design, develop, and deploy generative AI solutions using frameworks like LangChain and LlamaIndex. - Utilize advanced prompt engineering techniques to optimize LLM responses and work with advanced LLM features. - Implement RAG workflows by integrating vector databases such as Pinecone, Weaviate, Supabase, or PGVector. - Develop solutions using embeddings and similarity search for intelligent query resolution. - Process and analyze multimodal data for AI-driven applications. - Integrate observability and monitoring tools to track LLM performance. Backend Engineering: - Build and maintain scalable, secure backend services using Python frameworks like FastAPI, Django, or Flask. - Design and implement RESTful APIs for system integration. - Optimize and manage relational and vector databases for AI workflows. - Implement asynchronous programming patterns and follow clean coding practices. - Integrate third-party SDKs and APIs for system interoperability. - Develop backend pipelines for multimodal data processing. - Manage background tasks using Celery, cron jobs, or job queues. - Utilize Docker for reproducible deployments. - Ensure backend systems are secure and scalable for production. Qualifications: Essential Skills: - Proficiency in Python and backend frameworks. - Hands-on experience with Generative AI frameworks and RAG architectures. - Understanding of LLMs, embeddings, and similarity search techniques. - Experience with relational and vector databases. - Deployment of AI solutions using Docker. - Skills in testing, debugging, and performance optimization. - Understanding of asynchronous programming. Preferred Skills: - Experience with cloud platforms like AWS, GCP, or Azure. - Basic knowledge of frontend technologies. - Experience with observability and monitoring tools. - Awareness of emerging trends in generative AI and multimodal AI. - Knowledge of secure coding practices. - Relevant certifications in AI, Machine Learning, or Cloud technologies.