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.