Posted on: 14/10/2025
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.
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