SigFig - Full Stack Engineer - LLM
7 days ago
Description : The Engineering Team designs, builds, and maintains the systems that power Tandems' AI-enabled platform, which reimagines how financial advice is delivered, how institutions operate, and how advisors help clients succeed.Responsibilities : - Client-facing applications that enable seamless onboarding, personalised advice, and digital collaboration.- Algorithms and investment systems that support smarter portfolio management and better financial outcomes.- AI-powered tools that give advisors intelligent insights, streamline workflows, and enhance client engagement across our products : TandemsInvest, TandemsMeet, and TandemsGrow.Requirements : - 3+ years of software engineering experience with production systems.- Strong proficiency in Python and hands-on experience with LLM application frameworks (LangChain, LlamaIndex, Semantic Kernel, or DSPy).- Proven experience building agent workflows and orchestrating multi-step AI processes.- Deep understanding of foundation model APIs (OpenAI, Anthropic, Google AI, AWS Bedrock, Azure OpenAI, or open-source models via Replicate/Hugging Face).- Experience with prompt engineering, including techniques like few-shot learning, chain-of-thought, and prompt chaining.- Hands-on experience implementing RAG (Retrieval-Augmented Generation) systems with vector databases.Agent Development Experience : - Building autonomous and semi-autonomous agent systems that can plan, use tools, and execute multi-step tasks.- Implementing tool-calling and function-calling patterns with LLMs.- Creating agent memory systems (conversation history, long-term memory, knowledge bases).- Orchestrating multi-agent systems or agent handoffs.- Experience with agent evaluation, debugging, and observability (using tools like LangSmith, Weights & Biases, or custom solutions).Foundation Model Integration : - Managing different foundation models for specific tasks (routing between models based on complexity/cost/speed).- Implementing streaming responses and real-time user interactions.- Handling context window management and conversation pruning strategies.- Building fallback and error recovery mechanisms for AI systems.- Cost optimisation and rate limit management across multiple model providers. (ref:hirist.tech)
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