Architect - Machine Learning

Role: Senior Architect - Machine Learning

Experience Level: 13+ Years

Work location: Bangalore OR Mumbai (Hybrid)

We are seeking a Senior Architect - Machine Learning with deep expertise in Artificial Intelligence, Generative AI (GenAI), and Data Engineering to lead the design and implementation of cutting-edge AI solutions for enterprise use cases. The ideal candidate will bring strong experience in architecting scalable ML systems, hands-on knowledge of RAG, agent-based frameworks, document digitization, and multi-modal AI, and a strategic mindset to work with cross-functional stakeholders.

Role & Responsibilities:

Architecture & Solution Design

• Lead the design of enterprise-grade AI/ML architectures with high scalability, security, and maintainability.
• Architect GenAI-based applications using RAG (Retrieval-Augmented Generation), fine-tuned LLMs, multimodal AI, document understanding, and intelligent agent frameworks.
• Design end-to-end ML pipelines including data ingestion, processing, model training, evaluation, monitoring, and retraining.
• Define reusable AI components and services to support a multi-tenant, multi-use case platform strategy.

Technical Leadership

• Provide technical leadership in solutioning, technology stack decisions, and implementation strategies.
• Mentor and guide data scientists, ML engineers, and GenAI application developers across various teams.
• Stay current on advances in LLMs, foundation models, open-source libraries (LangChain, LlamaIndex), and transformer-based architectures.

GenAI & LLM-Based Innovation

• Drive development of RAG pipelines with document chunking, vector DB indexing (Pinecone, FAISS, Weaviate, Milvus), and semantic search.
• Build and orchestrate LLM-powered agents with memory, tools, and planning (LangGraph, AutoGen, CrewAI, OpenAgents).
• Leverage external APIs (OpenAI, Claude, Gemini, Mistral, HuggingFace) and evaluate open-source/self-hosted model alternatives (e.g., LLaMA, Mistral, Mixtral).
• Architect solutions for document digitization and understanding using OCR (AWS Textract, Azure Form Recognizer), table extraction, metadata processing, and forgery detection using CV and AI.

Integration & Deployment

• Design and oversee ML model deployment strategies using Kubernetes, Docker, Vertex AI, SageMaker, or Azure ML.
• Implement MLOps practices, including CI/CD for ML, feature stores, model registries, and A/B testing frameworks.
• Ensure seamless integration with enterprise systems (ERP, CRM, Data Lakes, APIs) via scalable microservices.

Enterprise Enablement

• Work with product managers and business leaders to translate business problems into ML/AI solutions.
• Define and implement governance, explainability, and responsible AI practices.
• Contribute to AI platform roadmap, reusability strategy, and innovation frameworks.

Key Skills & Qualifications: (Must Have skills):

• Strong programming skills in Python, and familiarity with Java/Scala/Go as needed.
• Deep understanding of GenAI technologies: LLMs (GPT, Claude, LLaMA), prompt engineering, fine-tuning, adapters (LoRA/QLoRA/PEFT).
• Experience with RAG architectures, vector databases, embedding models (OpenAI, Cohere, HuggingFace Transformers).
• Experience with agentic frameworks (LangChain Agents, LangGraph, CrewAI, AutoGen).
• Hands-on with document intelligence workflows OCR, NLP, CV-based document layout analysis, form extraction, etc.
• Familiarity with cloud platforms (GCP, AWS, Azure) and AI-native services (Vertex AI, Bedrock, OpenAI API).
• MLOps tooling: MLFlow, Kubeflow, Airflow, Feast, TFX, BentoML, Ray Serve.