AI Software Engineer at Vichara Technologies, Inc. - Canada

Bogotá, Bogota D.C., Colombia -

Full Time

Start Date

Immediate

Expiry Date

31 Mar, 26

Salary

1500000.0

Posted On

31 Dec, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI Software Engineering, LLMs, GenAI, RAG Frameworks, Agentic AI, Fine-Tuning, Infra, MLOps, Monitoring, Python, SQL, REST APIs, Git, Pandas, NumPy

Industry

Information Technology & Services

Description

Company Description Vichara is a Financial Services focused products and services firm headquartered in NY and building systems for some of the largest i-banks and hedge funds in the world. Job Description Key Responsibilities 🔹 Architecture & System Design Architect, design, and lead multi-agent LLM systems using LangGraph, LangChain, and Promptfoo for prompt lifecycle management and benchmarking. Build Retrieval-Augmented Generation (RAG) pipelines leveraging hybrid vector search (dense + keyword) using LanceDB, Pinecone, or Elasticsearch. Define system workflows for summarization, query routing, retrieval, and response generation, ensuring minimal latency and high precision. Develop RAG evaluation frameworks combining retrieval precision/recall, hallucination detection, and latency metrics — aligned with analyst and business use cases. 🔹 AI Model Integration & Fine-Tuning Integrate GPT-4o, PaLM 2, and open-weight models (LLaMA, Mistral) for task-specific contextual Q&A. Fine-tune transformer models (BERT, SentenceTransformers) for document classification, summarization, and sentiment analysis. Manage prompt routing and variant testing using Promptfoo or equivalent tools. 🔹 Agentic AI & Orchestration Implement multi-agent architectures with modular flows — enabling task-specific agents for summarization, retrieval, classification, and reasoning. Design fallback and recovery behaviors to ensure robustness in production. Employ LangGraph for parallel and stateful agent orchestration, error recovery, and deterministic flow control. 🔹 Data Engineering & RAG Infrastructure Architect ingestion pipelines for structured and unstructured data — including financial statements, filings, and PDF documents. Leverage MongoDB for metadata storage and Redis Streams for async task execution and caching. Implement vector-based search and retrieval layers for high-throughput and low-latency AI systems. 🔹 Observability & Production Deployment Deploy end-to-end AI systems on AWS EKS / Azure Kubernetes Service, integrated with CI/CD pipelines (Azure DevOps). Build comprehensive monitoring dashboards using OpenTelemetry and Signoz, tracking latency, retrieval precision, and application health. Enforce testing and regression validation using golden datasets and structured assertion checks for all LLM responses. 🔹 Cross-functional Collaboration Collaborate with DevOps, MLOps, and application development teams to integrate AI APIs with React / FastAPI-based user interfaces. Work with business analysts to translate credit, compliance, and customer-support requirements into actionable AI agent workflows. Mentor a small team of GenAI developers and data engineers in RAG, embeddings, and orchestration techniques. Qualifications Experience: 5+ years as an AI or ML Engineer Required Skills & Experience LLMs & GenAI: GPT-4o, PaLM 2, LangGraph, LangChain, Promptfoo, SentenceTransformers RAG Frameworks: LanceDB, Pinecone, ElasticSearch, FAISS, MongoDB Agentic AI: LangGraph multi-agent orchestration, routing logic, task decomposition Fine-Tuning: BERT / domain-specific transformer tuning, evaluation framework design Infra & MLOps: FastAPI, Docker, Kubernetes (EKS/AKS), Redis Streams, Azure DevOps CI/CD Monitoring: OpenTelemetry, Signoz, Prometheus Languages & Tools: Python, SQL, REST APIs, Git, Pandas, NumPy 🧠 Nice-to-Have Skills Knowledge of Reranker-based retrieval (MiniLM / CrossEncoder) Familiarity with Prompt evaluation and scoring (BLEU, ROUGE, Faithfulness) Domain exposure to Credit Risk, Banking, and Investment Analytics Experience with RAG benchmark automation and model evaluation dashboards Additional Information Compensation: COP1200000 - COP1500000 - yearly

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Responsibilities

The AI Software Engineer will architect and design multi-agent LLM systems and build RAG pipelines for high precision and minimal latency. They will also collaborate with cross-functional teams to integrate AI APIs and mentor junior developers.