AI Engineer

• Ship AI features: Build production-ready LLM applications (RAG pipelines, agents, orchestration layers) that deliver real business impact.
• Ingest & normalize: Design and implement data ingestion and transformation pipelines for diverse inputs.
• Stand up retrieval: Implement and optimize vector databases (Qdrant, Milvus, Pinecone, pgvector), define chunking/embedding strategies, and improve retrieval quality.
• Orchestrate agents: Build and optimize multi-step, agentic workflows using frameworks like LangChain/LangGraph, Agno, or CrewAI.
• Tune & optimize: Improve prompts, fine-tune open-source models for specific use cases, and optimize inference stacks for speed, cost, and reliability.
• Engineer context: Design and optimise context injection strategies (prompt templates, retrieval augmentation, dynamic context windows, memory management) to improve quality, reliability, safety, and cost-effectiveness of LLM responses.
• Observability & evals: Use tools like LangFuse, LangSmith (or similar) to monitor pipelines, run automated evals, and track cost, latency, and quality.
• Secure the stack: Defend against prompt injection, data leakage, and unsafe outputs by implementing guardrails, monitoring, and escalation/fallback mechanisms.
• Collaborate: Work closely with Data and Product to identify opportunities and deliver high-value AI capabilities.
• Learn & adapt: Stay ahead of the curve in LLM orchestration, observability, security practices and emerging open-source frameworks.

AI Engineer – Fintech

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Senior AI Developer

AI Engineer

AI Architect

AI Developer