Generative AI Machine Learning Engineer – LLM RAG & AI Agents

Square One Resources REMOTE 2026-10-01

Requirements

Strong experience in Machine Learning and Generative AI, particularly LLMs, NLP or multimodal models.
Good understanding of Deep Learning concepts.
Strong Python programming skills and knowledge of object-oriented programming.
Working knowledge of SQL and vector databases.
Hands-on experience with Azure or GCP.
Practical knowledge of PyTorch, NumPy, Hugging Face, LangChain and LangGraph.
Experience integrating OpenAI or Gemini APIs.
Proven experience designing and implementing RAG solutions.
Hands-on experience with MCP servers and clients for LLM-based agents.
Experience with microservice architectures and AI application deployment.
Ability to translate business needs into technical solutions.
Strong analytical, problem-solving and communication skills.

Nice to Have

Experience with Databricks.
Commercial experience delivering Generative AI, NLP or Computer Vision projects.
Experience with multimodal AI and production-grade AI agents.
Experience in technical consulting or pre-sales.
Ability to mentor and guide junior engineers.

Project Description

We are looking for a Senior Generative AI Engineer to join a project focused on developing advanced AI solutions, including chatbots, voicebots, AI agents and Talk-to-Data systems.

You will be responsible for designing and implementing end-to-end Generative AI solutions, from data processing and RAG pipelines to LLM integration and deployment. The role also involves working closely with business stakeholders, contributing to solution architecture and supporting pre-sales activities.

,[Design and develop end-to-end GenAI applications, including chatbots, voicebots and AI agents., Build and optimize RAG pipelines, including vector databases, hybrid search, reranking and retrieval evaluation., Develop LLM-based solutions using LangChain, LangGraph and LlamaIndex., Select, fine-tune and optimize LLMs using techniques such as LoRA, QLoRA and SFT., Implement LLM integrations with external tools, APIs and data sources using Model Context Protocol (MCP)., Develop and evaluate prompting strategies, guardrails and model performance., Translate business requirements into technical solutions and define project success metrics., Support AI solution deployment and project delivery., Contribute to technical architecture and pre-sales activities., Provide technical guidance and support to junior team members.] Requirements: Machine learning, AI, NLP, Python, Object-oriented programming, Azure, PyTorch, NumPy, Microservice architecture, Communication skills, Computer vision