What are the responsibilities and job description for the AI Solution Development position at Lorven Technologies Inc.?
Our client is looking AI Solution Development Engineer for Long Term project in Atlanta, GA/Syracuse, NY/Indianapolis, IN (Onsite) Below is the detail requirement.
Title- AI Solution Development
Location– Atlanta, GA/Syracuse, NY/Indianapolis, IN (Onsite)
Job Description:
Agentic AI is must to have
Agentic AI, Multi Agents & MCP:
- Collaborate with engineering teams to design MCP-based integrations and other integrations for internal tool development.
- Enable agent-driven workflows that streamline engineering processes across software, hardware, and mechanical domains.
AI Solution Development & Deployment:
- Design, develop, and deploy AI-driven solutions for engineering applications
- Designing scalable, production-ready AI systems that integrate LLMs like GPT-4, Google Gemini, Claude, or Llama with internal data and APIs.
- Building complex workflows using frameworks like LangChain to manage prompt chaining, memory, and multi-agent systems.
- Retrieval-Augmented Generation (RAG): Implementing vector databases (e.g., Pinecone, FAISS) to allow models to access and reason.
- Prompt Engineering: Refining and optimizing high-quality prompts to ensure model outputs are accurate, safe, and aligned with business requirements.
- Model Fine-Tuning: Using specialized techniques like LoRA (Low-Rank Adaptation) to adapt foundational models for niche domain-specific tasks.
- Evaluation & Monitoring: Establishing robust frameworks to test model performance against benchmarks for accuracy, bias, and reliability.
- Integrate AI capabilities into internal engineering tools to enhance productivity and automation.
- Take ownership, design and lead project for internal customer stakeholders.
LLMOps & Testing:
- Apply LLMOps best practices for lifecycle management of large language models, including CI/CD pipelines, monitoring, and governance.
- Develop and execute testing strategies for AI applications to ensure reliability, accuracy, and compliance.
Cloud AI Services Integration:
- Deploy and manage AI solutions on AWS, ensuring scalability, security, and cost optimization.
- Implement containerization, orchestration, and serverless architectures for AI workloads.
Collaboration & Documentation:
- Work closely with multidisciplinary teams in a global environment.
- Produce clear technical documentation and contribute to knowledge-sharing initiatives.