Senior Staff Engineer, Generative AI

Accountabilities:: Understand client business use cases and technical requirements and translate them into scalable, practical technical designs. Architect and implement end-to-end Generative AI and Agentic AI solutions aligned with functional and non-functional requirements. Design and implement RAG architectures, prompt engineering solutions, LLM applications, and AI agents for enterprise use cases. Evaluate alternative technical approaches and identify solutions that best address client requirements, scalability, security, maintainability, and performance considerations. Define technical guidelines, architecture standards, and benchmarks for non-functional requirements throughout project implementation. Create and review architecture, framework, and high-level design documentation to provide clear implementation guidance to development teams. Review solutions across extensibility, scalability, security, design patterns, user experience, performance, and other non-functional requirements. Select appropriate technologies, frameworks, design patterns, AI services, and infrastructure to deliver robust solutions. Develop high-quality, production-ready Python code and remain hands-on throughout solution implementation. Build and expose APIs using FastAPI and integrate AI applications with databases through ORM-based approaches. Productionize and scale AI systems on Azure or AWS, ensuring enterprise-grade reliability, performance, security, and maintainability. Implement and evaluate RAG, LLM, fine-tuning, distillation, and model evaluation approaches. Conduct proofs of concept to validate proposed architectures, technologies, and approaches before implementation. Analyze and resolve complex technical issues through systematic root-cause analysis and clearly communicate the rationale behind architectural and implementation decisions. Translate architecture and design decisions into actionable guidance for developers and engineering teams. Collaborate with product and engineering stakeholders to convert business requirements into AI-driven solutions. Establish scalable approaches capable of supporting enterprise GenAI workloads and live production environments. Contribute to the adoption of emerging AI technologies and practices, including Model Context Protocol (MCP) and open-source GenAI initiatives. Requirements 8+ years of total professional experience in software engineering, AI/ML, data science, or closely related technical disciplines. Bachelor’s or Master’s degree in Computer Science, Information Technology, or a related field. Deep understanding of LLMs and Transformer-based architectures, including models such as GPT, Llama, Claude, Gemini, Qwen, Mistral, and BERT-family models. Expert-level prompt engineering skills and strong hands-on experience implementing RAG patterns. Strong proficiency in Python and AI/ML technologies and libraries such as LangChain, LlamaIndex, LangGraph, LangSmith, Hugging Face Transformers, Scikit-learn, PyTorch, and TensorFlow. Strong experience with fine-tuning, model distillation, model evaluation, and production ML development. Strong knowledge of Python, SQL, Pandas, SciPy, and Scikit-learn, with experience in ML model development, validation, deployment, and tuning. Hands-on experience implementing anomaly-detection solutions. Experience with Snowflake Data Cloud and Snowflake Cortex AI. Strong experience with managed AI/ML services on cloud platforms such as Azure Machine Learning Studio or AI Foundry. Strong understanding of vector databases and retrieval technologies, including platforms such as Weaviate and Neo4j. Knowledge of GenAI evaluation metrics and methodologies, including BLEU, ROUGE, perplexity, semantic similarity, and human evaluation. Proven experience architecting and coding scalable GenAI and Agentic AI solutions rather than working solely in an advisory or architecture capacity. Ability to write high-quality, production-ready Python with strong testing, maintainability, and engineering practices. Proven experience productionizing AI systems on Azure or AWS and scaling AI solutions in live enterprise environments. Experience building and exposing APIs with FastAPI and integrating applications with databases using ORM technologies. Demonstrated delivery of at least one production Generative AI or Agentic AI solution. Familiarity with Model Context Protocol (MCP) is required. Contributions to open-source Generative AI projects are an asset. Strong architectural thinking, problem-solving, analytical, and root-cause analysis skills. Excellent communication skills and the ability to collaborate effectively across product, engineering, development, and client-facing teams. Benefits Opportunity to work on enterprise-scale Generative AI and Agentic AI solutions. Hands-on senior technical role combining architecture, software engineering, and AI/ML development. Exposure to leading LLM technologies, RAG architectures, AI agents, model evaluation, and modern AI frameworks. Opportunity to work across Azure and AWS cloud environments and enterprise AI/ML services. Exposure to Snowflake Data Cloud and Snowflake Cortex AI. Opportunity to contribute to proofs of concept, architectural standards, technical strategy, and production AI implementations. Collaborative environment involving product, engineering, developers, and client stakeholders. Opportunity to work with emerging technologies such as MCP and contribute to open-source GenAI initiatives. Full-time opportunity based in India.