Title - AI Solution Development

Location - Atlanta/Syracuse/Indianapolis (2 open positions).

Job Description:

Experience: 5 -7 Years

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.