SDE III - Gen AI

Responsibilities:

• Design and implement production-ready generative AI applications that serve millions of users, from initial architecture through deployment and monitoring.
• Build advanced RAG (Retrieval-Augmented Generation) pipelines that combine vector databases, hybrid search, and intelligent caching to deliver sub-second response times.
• Develop multimodal AI systems that seamlessly integrate text, vision, and audio capabilities using state-of-the-art models.
• Architect scalable microservices that handle thousands of concurrent AI requests while optimizing for cost, latency, and reliability.
• Lead code reviews and technical design sessions, establishing best practices and architectural patterns that elevate the entire team's capabilities.
• Optimize large language models through fine-tuning techniques to achieve domain-specific performance improvements.
• Implement comprehensive MLOps practices, including automated testing, model versioning, A/B testing frameworks, and real-time monitoring dashboards.
• Collaborate with product managers and stakeholders to translate complex business requirements into innovative AI solutions.
• Deploy AI models across multiple cloud platforms (GCP) using containerization and orchestration technologies.
• Create and maintain technical documentation, runbooks, and architectural decision records that enable knowledge sharing across teams.
• Mentor junior engineers through pair programming, technical talks, and hands-on guidance to accelerate their growth.
• Research and prototype emerging AI technologies to identify opportunities for competitive advantage.

Gen AI Responsibilities:

• Fine-tune and optimize state-of-the-art language models for specific business use cases, achieving significant improvements in accuracy and relevance.
• Design multi-agent AI systems using frameworks to orchestrate complex workflows and decision-making processes.
• Implement advanced prompt engineering strategies, including Tree of Thoughts, ReAct patterns, and automatic prompt optimization, to maximize model performance.
• Build production-grade embedding systems that handle billions of vectors, implementing efficient indexing strategies and hybrid search capabilities.
• Develop computer vision pipelines using models for tasks ranging from object detection to visual question answering.
• Create secure AI applications with robust safeguards against prompt injection, jailbreaking, and data leakage while maintaining compliance with AI governance standards.
• Optimize token usage and implement intelligent caching strategies to reduce costs by 50-70% while maintaining quality.
• Design and implement evaluation frameworks that go beyond traditional metrics, incorporating human feedback loops and domain-specific quality measures.
• Build real-time AI inference systems capable of processing streaming data with sub-100ms latency requirements.
• Integrate multiple foundation models into unified applications, implementing fallback mechanisms and load balancing for high availability.
• Develop custom tools and functions that extend LLM capabilities, enabling models to interact with databases, APIs, and external systems.
• Implement advanced RAG techniques, including contextual embeddings, cross-encoder reranking, and Graph RAG for complex reasoning tasks.
• Create multimodal search systems that enable users to query across text, images, and documents using natural language.
• Build AI-powered data processing pipelines that automatically extract, transform, and enrich unstructured data at scale.
• Deploy edge AI solutions using frameworks like ONNX and TensorRT, optimizing models for resource-constrained environments.

Requirements:

• 5+ years of hands-on experience building and deploying ML/AI systems, with at least 2+ years focused on generative AI and LLMs.
• Expert-level Python programming skills with deep knowledge of async programming, multiprocessing, and performance optimization.
• Strong experience with modern AI frameworks including PyTorch, Transformers, LangChain, and vector databases.
• Proven track record of deploying AI applications to production environments serving real users at scale.
• Deep understanding of transformer architectures, attention mechanisms, and the latest advances in generative AI.
• Experience with cloud platforms (GCP) and containerization technologies (Docker, Kubernetes).
• Excellent communication skills with the ability to explain complex AI concepts to both technical and non-technical audiences.
• Proven experience improving large-scale product search and discovery, including dense retrieval with bi-encoders, cross-encoder reranking, query understanding, and hybrid BM25 + vector search across catalogs of tens of millions of SKUs.
• Hands-on experience building and deploying production multi-agent systems using orchestration frameworks such as LangGraph and Google ADK designing stateful, tool-augmented agents for complex, real-world workflows.
• Bachelor's degree in Computer Science, Mathematics, or a related field (a Master's is preferred but not required with relevant experience).

Nice to Have:

• Published research papers or significant contributions to open-source AI projects.
• Experience with multimodal AI systems combining vision, language, and audio.
• Domain expertise in specific verticals (healthcare, finance, legal, e-commerce).
• Knowledge of AI safety, alignment, and constitutional AI principles.
• Experience building AI infrastructure and platforms used by other engineers.
• Familiarity with emerging technologies like neural architecture search, mixture of experts, or neuromorphic computing.