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Lead AI Engineer

Javen Technologies, IncUnited States🇺🇸United StatesPosted 31 Jul 2026

Why This Role Stands Out

This remote Lead AI Engineer role at Javen Technologies offers a fantastic opportunity to design and build cutting-edge AI agent systems and LLM-based applications, with significant potential for professional growth in a rapidly evolving field. If you possess strong AI knowledge, Python development skills, and experience with cloud AI services, you'll thrive in this position, contributing to innovative enterprise solutions.

Quick Overview

Work Type
Remote
Level
Mid Senior

Job Description

Title: Lead AI Engineer

Shift: M-F, shift is as early as possible to have overlap with India and the UK
Interviews: Virtual Interviews
Location: Remote, preference is in Eastern region
Candidate Qualities: good developer, AI knowledge, will function as a lead, implementation framework

Job Description
This position will design and build intelligent AI agent systems, LLM-based applications, and autonomous workflow solutions within the context of our enterprise applications. We are looking for a candidate to provide expertise in Large Language Models, multi-agent architectures, RAG systems, Python development, cloud AI services, and enterprise AI integration. This individual will have broad experience in developing and deploying agentic AI solutions that can autonomously perform complex business tasks.

Responsibilities

  • 4-6 years hands-on experience working as Sr AI/ML Developer with at least three complete agentic AI system implementations
  • 4-6 years hands-on experience with cloud AI services (Azure OpenAI, AWS Bedrock, Google Vertex AI)
  • 4-6 years hands-on experience with Python AI/ML frameworks (LangChain, LlamaIndex, Transformers, PyTorch)
  • 4-6 years hands-on experience integrating LLMs with external systems and enterprise applications
  • Experience working with vector databases, knowledge graphs, and RAG pipeline development
  • Advising on best practices for AI agent development and enterprise AI integration processes
  • Experience in deploying AI models and agents in multiple environments (dev, staging, production)
  • Experience managing stakeholder communication regarding AI capabilities, limitations, and project timelines, including managing expectations, foreseeing AI-related risks and reporting them

Experience & Skills

  • Proficient in developing, deploying, and orchestrating multi-agent AI systems
  • Demonstrated proficiency in LLM fine-tuning, prompt engineering, and model optimization
  • Demonstrated proficiency in designing and implementing RAG (Retrieval-Augmented Generation) systems
  • Demonstrated proficiency in understanding and implementing autonomous business workflows and AI-driven processes
  • Demonstrated proficiency in using AI frameworks like LangChain, LlamaIndex, and Hugging Face ecosystem
  • Demonstrated proficiency with Python development and AI/ML libraries
  • Demonstrated proficiency in JavaScript/TypeScript for AI frontend integration
  • Ability to perform AI model performance tuning and optimization in production environments
  • Familiarity with MLOps tools and practices
  • Experience in building conversational AI interfaces, chatbots, and AI-powered applications
  • Experience with API development for AI services and webhook management
  • Experience with cloud AI platforms (Azure OpenAI, AWS Bedrock, Google Cloud AI)
  • Experience in AI agent orchestration, planning algorithms, and decision-making frameworks
  • Creating and maintaining vector databases and knowledge management systems
  • Experience producing AI system architecture and technical design documentation
  • Must have college degree in Computer Science, AI/ML, or equivalent experience

Technical Skills & Competencies

  • Python, FastAPI, Flask
  • LangChain, LlamaIndex, Transformers library
  • OpenAI API, Azure OpenAI, Anthropic Claude
  • Vector databases (Pinecone, Weaviate, ChromaDB)
  • Git, Docker, Kubernetes
  • JavaScript/TypeScript for AI integration
  • Postman for API testing
  • SQL/NoSQL databases for AI data management
  • Sound knowledge in cloud AI services and MLOps
  • AI model performance monitoring and optimization
  • Prompt engineering and AI safety practices

Uses best practices and knowledge of AI/ML methodologies to improve products and services through intelligent automation. Solves complex business problems by designing novel AI agent solutions that combine existing technologies in innovative ways.

Skills

Docker
FastAPI
Flask
SQL
AWS
MLOps
Azure
Git
Google Cloud
Hugging Face
JavaScript
Kubernetes
LLM
Postman
PyTorch
Python
TypeScript

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