• Design, develop, and deploy AI/ML models for production environments. • Build and optimize AI-powered applications using large language models (LLMs) and generative AI tools. • Develop intelligent automation workflows, chatbots, recommendation systems, and predictive analytics solutions. • Fine-tune, evaluate, and improve machine learning models for performance and scalability. • Collaborate with software engineers and product teams to integrate AI features into existing platforms. • Work with structured and unstructured datasets for training and inference pipelines. • Implement prompt engineering strategies and retrieval-augmented generation (RAG) systems. • Monitor AI systems, troubleshoot issues, and continuously improve model accuracy and efficiency. • Stay updated with the latest advancements in AI, machine learning, and automation technologies. • Maintain documentation for AI models, systems, and workflows.

• Design, develop, and deploy AI and machine learning models for real-world applications. • Build and optimize AI-powered tools using technologies such as OpenAI, Claude, Gemini, or open-source LLMs. • Develop scalable APIs and backend services for AI applications. • Fine-tune machine learning models and improve model performance. • Work with structured and unstructured datasets for training and inference. • Implement prompt engineering strategies and retrieval-augmented generation (RAG) pipelines. • Integrate AI solutions into web platforms, CRMs, and business systems. • Monitor AI model performance, reliability, and scalability in production. • Collaborate with cross-functional teams to define AI product requirements and technical roadmaps. • Stay updated on emerging AI technologies, frameworks, and best practices.

• Design, build, and deploy AI and machine learning solutions for real-world business applications. • Develop and maintain applications using Large Language Models (LLMs) such as GPT-based systems. • Build AI-powered automations, copilots, chatbots, and workflow systems. • Fine-tune, evaluate, and optimize machine learning models for performance and scalability. • Integrate AI systems with APIs, databases, CRMs, and third-party platforms. • Collaborate with product and engineering teams to translate business requirements into AI-driven solutions. • Implement prompt engineering strategies and retrieval-augmented generation (RAG) systems. • Monitor model performance, reliability, and cost efficiency in production environments. • Maintain documentation, testing procedures, and deployment workflows. • Stay updated on the latest AI trends, frameworks, and emerging technologies.

• Drive Technical Excellence: Act as a technical pillar within the Lab, implementing high-standard code and sophisticated system designs. You will mentor mid-level peers and lead deep-dive code reviews. • Architect Multi-Agent Systems: Design and deploy distributed systems and multi-agent architectures that automate complex engineering tasks. You will own the architectural decisions for "build vs. integrate" strategies. • Innovate with Agentic AI: Spearhead the evaluation and prototyping of LLMs, Agentic frameworks, and Model Context Protocols (MCPs). You will transform theoretical AI advancements into production-ready tools. • Own the Full Lifecycle: Take responsibility for the entire development cycle. From initial concept and API integration to production deployment and long-term scalability. • Influence Product Strategy: Partner with Product and Engineering Managers to ensure the Lab’s innovations align with the broader company roadmap and provide measurable ROI to our developers.