Straive - Senior Generative AI Developer - RAG/LLM

Job Description :

Straive is looking for a Senior GenAI Developer with a strong software engineering background and handson expertise in Generative AI, Large Language Models (LLMs), RetrievalAugmented Generation (RAG), prompt engineering, and cloudnative application development. The ideal candidate will be responsible for designing, developing, optimizing, and deploying productiongrade GenAI solutions for enterprise use cases. You will work closely with engineering, product, and business teams to translate complex requirements into scalable AIpowered applications. The role requires strong expertise in Python and modern AI/ML frameworks, along with experience building reliable, secure, and highperformance LLM applications.

Key Responsibilities :

• Design and architect scalable Generative AI applications using LLMs and modern AI frameworks.

• Build and optimize RetrievalAugmented Generation (RAG) pipelines for enterprise applications.

• Develop document ingestion, preprocessing, chunking, embedding, retrieval, reranking, and responsegeneration workflows.

• Implement advanced Prompt Engineering techniques, including fewshot prompting, structured prompting, prompt chaining, and reasoningbased approaches.

• Integrate commercial and opensource LLMs such as OpenAI, Anthropic, Llama, and Mistral into enterprise applications.

• Develop and implement LLM finetuning solutions using LoRA/QLoRA and evaluate model performance.

• Work with vector databases such as Pinecone, ChromaDB, or equivalent technologies.

• Build robust APIs and backend services using Python, RESTful APIs, and/or GraphQL.

• Optimize GenAI applications for latency, scalability, accuracy, reliability, and cost efficiency.

• Develop evaluation frameworks to measure LLM response quality, relevance, hallucination, latency, and retrieval performance.

• Implement techniques to improve RAG accuracy, including metadata filtering, hybrid search, reranking, query transformation, and contextual retrieval.

• Deploy AI applications using cloud platforms such as AWS, Azure, or GCP.

• Containerize applications using Docker and deploy/manage workloads using Kubernetes.

• Implement CI/CD pipelines and follow DevOps best practices for production deployments.

• Ensure enterprise applications follow appropriate security, privacy, governance, and accesscontrol practices.

• Troubleshoot production issues and continuously improve application performance.

• Conduct code reviews and establish software engineering best practices.