AI Software Engineer

Aimlroles; Bogotá; On-site; COP 60,000,000 - 120,000,000; Full time

Job summary

Vichara is seeking an experienced AI/ML Engineer to architect and lead multi-agent LLM systems for financial services. You will design RAG pipelines, integrate GPT-4o and PaLM 2, and ensure low latency with robust orchestration.

You will collaborate with DevOps and application teams to deploy on AWS EKS/Azure Kubernetes, implement observability, and drive model evaluation, retrieval quality, and reliability.

Qualifications

• 5+ years as an AI/ML Engineer or similar role.
• Experience with multi-agent AI systems and RAG pipelines.
• Hands-on with LLMs (GPT-4o, PaLM 2) and open-weight models.
• Proficient in MLOps tooling and cloud deployments.

Responsibilities

• Architect and lead multi-agent LLM systems with prompt lifecycle management.
• Build RAG pipelines with hybrid vector search.
• Integrate models and fine-tune transformers for classification and summarization.
• Design and implement agent orchestration and robust fallback behaviors.
• Collaborate across DevOps, MLOps, and UI teams to deliver production-grade AI apps.

Skills

GPT-4o

PaLM 2

LangGraph

LangChain

Promptfoo

SentenceTransformers

LanceDB

Pinecone

Elasticsearch

FAISS

MongoDB

OpenTelemetry

Signoz

Prometheus

Python

SQL

REST APIs

Git

Pandas

NumPy

FastAPI

Docker

Kubernetes

Redis Streams

Azure DevOps

Tools

AWS EKS

Azure Kubernetes Service

Job 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.

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.

Required Skills & Experience

• Experience:
  • 5+ years as an AI or ML Engineer

• 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