AI Software Engineer
At a glance
Mid-level AI Engineering role at Vichara. Bogotá · full-time.
Pay not stated AI Software Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Design multi-agent LLM systems and RAG workflows for summarizing, routing queries, retrieving information, and generating responses
- Build RAG evaluation that checks retrieval quality, hallucinations, and latency against analyst and business needs
- Connect language models and fine-tune transformer models for document classification, summaries, sentiment analysis, and contextual Q&A
- Create data ingestion and retrieval infrastructure for financial statements, filings, and PDFs, using metadata storage and async processing
- Deploy and monitor AI systems on Kubernetes, validate responses with test datasets, and guide developers and data engineers
What you bring
- At least five years working as an AI or machine learning engineer
- Hands-on experience with LLMs, LangGraph, LangChain, Promptfoo, and SentenceTransformers
- Experience building RAG systems and multi-agent workflows, including retrieval, routing, and task decomposition
- Background fine-tuning transformer models and designing evaluation frameworks
- Practical skills with Python, FastAPI, Kubernetes, Redis Streams, Azure DevOps, and AI-system monitoring tools
Who this fits
This role suits a mid-level AI engineer who can build and deploy LLM and RAG systems, while also mentoring a small technical team. The job is based in Bogotá and follows a hybrid schedule.
From the employer
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.
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.
- 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