Position Description: Company Profile:
Job Title: GenAI Solution Architect / Lead GenAI Engineer Position: Lead Analyst Experience: 6 Plus Category: Software Development Main location: Bangalore Position ID: J1025-1909 Employment Type: Full Time
Your future duties and responsibilities: Job Summary: We are seeking a highly skilled and innovative GenAI Solution Architect / Lead AI Engineer to design, develop, and optimize Generative AI applications and system architectures. The ideal candidate will have extensive experience in Python, Azure Cloud services, and LLM frameworks (such as vLLM). This role requires a strong foundation in system design, performance optimization (throttling, queuing, refactoring), and the ability to architect scalable AI solutions that deliver real-world business impact.
Key Responsibilities:Architecture & Design
. Design and implement end-to-end GenAI solutions using Azure OpenAI Services, Azure Machine Learning, and related Azure AI offerings. . Create scalable and modular architectures for Generative AI systems, integrating LLMs and prompt orchestration frameworks (e.g., LangChain, Semantic Kernel). . Define system design patterns and ensure alignment with performance, reliability, and security standards. . Develop and maintain reference architectures and best practices for GenAI solution design and deployment. Development & Implementation . Build, fine-tune, and deploy LLM-based applications using frameworks such as vLLM, Hugging Face Transformers, or PyTorch. . Implement throttling, caching, and queuing mechanisms to optimize performance and manage compute resource utilization. . Lead code refactoring and optimization efforts to enhance efficiency, maintainability, and scalability. . Integrate APIs, data pipelines, and vector databases (e.g., FAISS, Pinecone, Azure Cognitive Search) into GenAI workflows. Collaboration & Mentorship . Collaborate with data scientists, cloud engineers, and product teams to translate business problems into GenAI-driven solutions. . Mentor junior developers on AI application design, coding standards, and architecture principles. . Participate in code reviews, solution walkthroughs, and performance tuning sessions. Required Skills & Experience:Technical Expertise
. Strong proficiency in Python with hands-on experience in building and optimizing AI/ML pipelines. . Experience with Azure Cloud Platform, including services such as Azure OpenAI, Azure Machine Learning, Azure Functions, and Azure Kubernetes Service (AKS). . Deep understanding of LLMs and inference optimization using vLLM, DeepSpeed, or similar frameworks. . Expertise in throttling, queuing systems, and asynchronous processing architectures (Celery, Kafka, RabbitMQ, Azure Service Bus, etc.). . Experience with code refactoring, performance tuning, and scalable software design principles. . Familiarity with vector databases, embedding models, and prompt engineering. Preferred Skills . Experience with LangChain, Semantic Kernel, or similar orchestration frameworks. . Knowledge of containerization (Docker) and orchestration (Kubernetes). . Understanding of MLOps, CI/CD pipelines, and monitoring for AI applications. . Experience integrating multi-modal AI components (text, image, or speech) in GenAI systems. Required qualifications to be successful in this role: Qualifications:
. Bachelor's or master's degree in computer science, Artificial Intelligence, or a related field. . 6+ years of experience in software development, with at least 2+ years in GenAI solutioning. . Proven experience in designing and deploying AI solutions on Azure Cloud. Other Skills . Communication and Collaboration: Clearly explain complex AI concepts to diverse audiences, including non-technical stakeholders. Work effectively with cross-functional teams like data scientists, engineers, and product managers to align on project goals. . Leadership and Strategic Thinking: Guide and mentor project teams while translating business requirements into a coherent technical vision. Design scalable, robust, and cost-effective AI systems and architectures. . Problem-Solving and Critical Thinking: Analyze complex challenges to devise innovative AI solutions. Critically evaluate AI outputs for accuracy, relevance, and potential issues, and troubleshoot problems within the AI pipeline. . Adaptability and Continuous Learning: Maintain a strong commitment to lifelong learning to stay current with the rapid evolution of AI technologies, tools, and best practices. Quickly adapt strategies and solutions as the field progresses. . Ethical and Responsible AI: Champion ethical AI development by proactively identifying and mitigating biases in models and data. Prioritize data privacy, security, and the creation of transparent and fair AI systems.
