AI/ML Architect

Role & 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.

Preferred candidate profile

• 7+ years of overall IT experience in designing, developing, deploying, and operationalizing AI/ML solutions.
• Minimum 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.