Ai/Ml Architect

Job Title: AI/ML Architect Location: Bangalore / Pune Experience: 10+ Years Job Type: Full-Time | Permanent Work Mode: Hybrid Job Summary We are seeking an experienced and visionary AI/ML Architect to lead the end-to-end design, development, deployment, and operationalization of advanced AI/ML and Generative AI (GenAI) solutions on cloud platforms. The ideal candidate will possess deep technical expertise in ML architecture, GenAI frameworks, Retrieval-Augmented Generation (RAG) pipelines, cloud-native deployment, and MLOps practices. You will work closely with cross-functional teams, clients, and engineering teams to define scalable AI strategies and deliver cutting-edge solutions across various domains. Key Responsibilities Customer Engagement & Solution Architecture - Interact with clients and stakeholders to gather business and technical requirements and translate them into scalable AI/ML solutions. - Architect and design AI/ML systems across AWS, GCP, or Azure with a strong focus on cloud-native and cost-optimized architecture. - Create detailed system design documents, architecture diagrams, and technical roadmaps. - Define data architecture, storage, and retrieval strategies tailored to AI/ML workflows. GenAI & RAG Architecture - Lead the design and implementation of Generative AI solutions using LLMs, LangChain, LlamaIndex, Prompt Engineering, and vector databases such as Pinecone, FAISS, Weaviate, or Elasticsearch. - Architect RAG (Retrieval-Augmented Generation) pipelines for enterprise use cases including knowledge management, chatbot development, and document summarization. - Implement prompt orchestration, retrieval optimization, and grounding techniques to enhance LLM output accuracy and relevance. AI/ML Model Development & MLOps - Guide the development of Python-based APIs, data preprocessing workflows, and model training pipelines. - Design and implement robust CI/CD pipelines for ML model deployment using tools like SageMaker, Vertex AI, or Azure ML. - Define and implement model monitoring, retraining, and performance management strategies for production-grade ML systems. - Ensure best practices in versioning, reproducibility, model lineage, and auditability (MLOps/LLMOps). Technical Leadership & Governance - Review and approve system designs, PoCs, and implementation approaches. - Provide hands-on leadership and mentorship to data scientists, ML engineers, and software developers. - Lead architectural decision-making, code quality reviews, and sprint grooming sessions. - Champion best practices in security, compliance, scalability, and performance optimization for AI/ML solutions. Project Management & Collaboration - Own end-to-end technical delivery of AI/ML and GenAI projects across multiple domains (e.G., BFSI, Retail, Healthcare, Manufacturing). - Coordinate with product owners, business analysts, data engineers, and DevOps teams to ensure seamless delivery. - Manage stakeholder expectations, project timelines, and resource allocation efficiently. Required Qualifications - 10+ years of overall IT experience, with minimum of 5+ years in designing, developing, deploying, and operationalizing AI/ML solutions. - Minimum 2–3 years of experience in architecting end-to-end AI/ML solutions, including design, implementation, and production deployment. - Proven experience in GenAI, LLMs, RAG architecture, prompt engineering, and orchestration tools like LangChain, LlamaIndex, etc. - Hands-on with vector databases (e.G., Pinecone, FAISS, Elasticsearch) and unstructured data retrieval. - Deep knowledge of Machine Learning and Deep Learning algorithms: CNNs, RNNs, LSTMs, Transformers, etc. - Experience in Natural Language Processing (NLP), including language modeling, summarization, classification, and NER. - Strong expertise in Python, with frameworks like PyTorch, TensorFlow, HuggingFace, NumPy, and Pandas. - Demonstrated experience in designing cloud-native AI/ML solutions on AWS, GCP, or Azure. - Skilled in deploying models via services like SageMaker, Vertex AI, Azure ML, or using containers and Kubernetes. - Solid understanding of MLOps/LLMOps lifecycle: pipeline automation, model registry, monitoring, CI/CD. - Excellent communication, leadership, and stakeholder management skills. Preferred Qualifications - Certification in AWS/GCP or ML specializations. - Experience in leading large-scale AI transformation programs. Why Join Us? - Work with cutting-edge GenAI and AI/ML technologies and projects. - Collaborate with top-tier clients and drive real-world impact. - Leadership opportunities in a growing AI/ML practice.