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Staff Back End Engineer, AI Products – San Francisco – Hybrid Up to $375k Base + Equity + Opportunity to work with an Ambitious, Young Growing Organisation! This young and agile company, building cutting edge financial services platforms is currently seeking Staff Back End Engineers with experience in AI Products to join their growing team. You will assist with the continuous expansion and optimization of their financial services platforms in a frontier space. This is an excellent opportunity for an experienced Software Engineer with experience in AI and LLM’s to take the next step into a challenging position with a company offering significant growth potential.
Job Responsibility:
Act as a leader and steward of engineering best practices, helping to mentor other engineers and develop impactful solutions
Analyze and solve complex problems spanning multiple user personas and workflows
Focus on building and scaling services and user facing web applications
Build customer centric scalable and performant AI-driven applications and features
Develop and maintain scalable data pipelines and ELT processes
Build new data integrations based on established requirements
Perform data analysis to investigate and resolve data issues
Requirements:
Several years of software engineering experience building precise and scalable distributed systems with best engineering practices
Experience with Generative AI frameworks and tools (RAG, Langchain, Vector database and other emerging agentic frameworks)
Prior experience delivering impactful Gen-AI products in production
Familiarity with AI/ML use cases such as content generation, summarization or natural language processing (NLP)
Familiarity with integrating third-party AI APIs and services using platforms like OpenAI, Hugging Face, and Anthropic
Ideally you have experience on Vector Databases and Python on the back-end
And TypeScript on the front-end
And experience leveraging external LLMs
Nice to have:
Prior experience steering AI model output using techniques such as prompt engineering, supervised finetuning, and reinforcement learning would be a bonus