AI Solution Development

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.