Sr AI Engineer

Role Overview: You will be responsible for researching and experimenting with the latest AI and cloud innovations, such as AWS Agents, Databricks AI, and other Model Context Protocol (MCP) integrations. Your role will involve prototyping and implementing these solutions using Python, developing proof-of-concepts, and driving the implementation of scalable AI-enabled applications. Key Responsibilities: - Research and evaluate emerging technologies in AI, cloud computing, and developer productivity, including AWS Core Agent, Databricks, LLM-based agents, and MCPs. - Prototype and integrate AI-driven solutions to enhance internal tools, chatbots, and workflows. - Design and develop robust, scalable Python-based systems for automating operational tasks and improving reliability. - Collaborate with onshore SRE and engineering teams to translate ideas into working implementations. - Maintain awareness of advancements in AI frameworks, open-source models, and productivity-enhancing platforms. - Build and maintain connectors, APIs, and scripts that integrate different systems via MCP or other automation frameworks. - Contribute to internal knowledge-sharing and documentation of AI experiments and integrations. - Own end-to-end testing, validation, and continuous improvement of AI-assisted systems. Qualification Required: - Strong programming skills in Python with hands-on experience in API integration, automation, or AI system development. - Familiarity with AI/ML concepts, LLMs, or modern AI frameworks such as LangChain, OpenAI API, or Hugging Face. - Strong research mindset with the ability to quickly understand, evaluate, and apply new technologies. - Interest in integrating with MCPs or similar interoperability frameworks. - Basic understanding of cloud technologies such as AWS, GCP, or Azure. - Understanding of observability, monitoring, or automation in production environments. - Good communication skills and ability to collaborate effectively across distributed (onshore/offshore) teams. - Experience with version control systems (Git) and agile development workflows. - Experience with LLM agent architectures, RAG pipelines, or vector databases. Additional Details: The company values prior exposure to Databricks, AWS AI services, or LangGraph/CoreAgent, familiarity with containerization (Docker/Kubernetes), and experience contributing to internal developer tools or productivity platforms.,