Gen AI Engineer

We are looking for a Gen AI Engineer to collaborate with a US-based client!

We seek an AI and AI Agent Developer/Engineer to design, develop, and deploy advanced AI agents that enhance enterprise automation, decision-making, and intelligent data processing. This role requires strong Python expertise, proficiency in AI agent orchestration frameworks (LangGraph, AutoGen, CrewAI), and hands-on experience with multi-modal models, retrieval-augmented generation (RAG/GraphRAG), and machine learning integrations. The ideal candidate is passionate about AI-driven automation, knowledge graphs, graph databases, and ontologies.

Key Responsibilities

1. AI Agent Development & Multi-Agent Orchestration

• Develop AI-powered agents using LangGraph, AutoGen, CrewAI, or similar frameworks to handle stateful, multi-agent collaboration.
• Implement multi-agent orchestration strategies, enabling AI agents to collaborate effectively and autonomously perform complex tasks.
• Engineer autonomous decision-making workflows, ensuring AI agents communicate and adjust based on dynamic conditions.
• Design and optimize state management for AI agents to enable seamless execution across long-running workflows.

2. Large Language Models (LLMs) & Multi-Modal AI

• Work with LLMs (GPT-4, Claude, LLaMA, Mistral) and fine-tune models to optimize task-specific performance.
• Integrate multi-modal AI models, including text-to-image, text-to-video, speech-to-text, and cross-modal transformers.
• Implement AI-powered retrieval systems using RAG and GraphRAG methodologies for contextualized knowledge extraction.
• Build and fine-tune custom AI models, leveraging Hugging Face Transformers, TensorFlow, and PyTorch.
• Work with vector databases (e.g., FAISS, Pinecone, Weaviate, ChromaDB) to optimize AI search capabilities.

3. API Development & Cloud Integration

• Develop AI-powered APIs and deploy AI models using FastAPI, Flask, or gRPC for scalable and efficient model serving.
• Integrate AI systems with cloud platforms prioritizing open-source solutions first, then GCP, AWS, and Microsoft Azure:
  • Google Cloud: Vertex AI, BigQuery ML, Cloud Run, AI Platform.
  • AWS: SageMaker, Bedrock, Lambda, DynamoDB.
  • Microsoft Azure: OpenAI Service, Cognitive Services, Synapse AI.
• Implement MLOps pipelines using Kubeflow, MLflow, Airflow, or Vertex AI Pipelines for model lifecycle automation.
• Build real-time AI inference solutions, leveraging Kafka, RabbitMQ, or Redis Streams.

4. Knowledge Graphs, Ontologies & Graph Databases

• Develop AI agents that interface with knowledge graphs and graph databases to enhance reasoning and retrieval.
• Leverage graph-based reasoning engines like Neo4j, TigerGraph, Amazon Neptune, or ArangoDB for AI inference.
• Design and implement semantic search capabilities, utilizing SPARQL, RDF, and OWL for ontological reasoning.
• Integrate RAG methods with graph-based knowledge systems to optimize retrieval efficiency.

5. Collaboration & Agile Development

• Work within Agile and Scaled Agile frameworks, ensuring AI project milestones align with business goals.
• Maintain Jira/Atlassian boards, tracking AI agent development progress and dependencies.
• Participate in code reviews, technical documentation, and sprint planning to enhance cross-functional collaboration.

Required Skills & Experience

• Proficiency in Python, with experience in AI/ML frameworks like TensorFlow, PyTorch, Hugging Face, and OpenAI APIs.
• Experience developing AI agents using LangGraph, AutoGen, CrewAI, or similar agent frameworks.
• Expertise in retrieval-augmented generation (RAG) and GraphRAG for knowledge-based AI applications.
• Hands-on experience with LLM integration in production environments, including fine-tuning and prompt engineering.
• API development experience, including FastAPI, Flask, GraphQL, and gRPC.
• Cloud AI experience, prioritizing open-source, followed by GCP, AWS, and Azure.
• Strong experience in knowledge graphs and graph databases such as Neo4j, ArangoDB, Amazon Neptune.

Preferred Qualifications

• Experience in AI Ethics & Responsible AI development, ensuring fairness and bias mitigation in AI models.
• Understanding of reinforcement learning (RLHF, PPO, or SAC) for training AI agents.
• Expertise in scalable AI deployment, using Ray, Dask, or Spark for distributed AI processing.
• Experience with MLOps and model versioning using Kubeflow, MLflow, or Airflow.
• Knowledge of distributed AI architectures (e.g., multi-agent RL, federated learning).
• Contributions to open-source AI projects or research publications in AI agent-based systems.
  
  Why Join Us?
  
  • Full-time position
  • 100% remote anywhere in LATAM
  • Payment in US dollars
  • 12 PTO per year
  • Holidays from your country off and paid
  • Birthday off and paid
  • Career Path
  • Recognition Program
  • Paid Leaves