What are the responsibilities and job description for the Sr GenAI Engineer position at RIIM?
Job Summary
We are seeking an experienced Senior GenAI Engineer to design, develop, and deploy enterprise-grade Generative AI solutions. The ideal candidate will have strong expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI frameworks, prompt engineering, vector databases, and cloud-based AI platforms. The role involves building scalable AI applications, mentoring team members, and collaborating with stakeholders to deliver innovative AI-powered solutions.
Key Responsibilities
- Design and develop GenAI applications using LLMs and multimodal AI models.
- Build and optimize RAG pipelines using vector databases and semantic search.
- Develop Agentic AI systems using frameworks such as LangChain, LangGraph, CrewAI, AutoGen, and LlamaIndex.
- Implement prompt engineering strategies and fine-tune foundation models for business use cases.
- Integrate AI solutions with enterprise systems, APIs, and cloud services.
- Develop scalable AI services and deploy them using MLOps and CI/CD practices.
- Configure AI guardrails, content moderation, and PII protection mechanisms.
- Monitor model performance, evaluation metrics, hallucination rates, and system observability.
- Collaborate with product managers, architects, and business stakeholders to define AI solutions.
- Mentor junior engineers and provide technical leadership across AI initiatives.
- Stay updated with emerging trends in Generative AI, LLMOps, and Agentic AI technologies.
Required Skills
Technical Skills
- Strong proficiency in Python.
- Experience with LLMs such as GPT, Claude, Llama, Mistral, and Gemini.
- Expertise in RAG, embeddings, vector search, and knowledge retrieval systems.
- Hands-on experience with LangChain, LangGraph, CrewAI, AutoGen, or similar frameworks.
- Experience with vector databases such as Pinecone, Weaviate, Milvus, Chroma, or Redis.
- Strong understanding of NLP, Machine Learning, Deep Learning, and Transformer architectures.
- Knowledge of cloud platforms: Azure, AWS, or Google Cloud Platform.
- Experience with MLOps, CI/CD, Docker, Kubernetes, and model deployment.
- Familiarity with SQL and NoSQL databases.
- Knowledge of AI safety, guardrails, governance, and responsible AI practices.