Architect - Machine Learning

Location: Bengaluru

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 (Lang Chain, Llama Index), 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 (Lang Graph, Auto Gen, CrewAI, Open Agents).
Leverage external APIs (OpenAI, Claude, Gemini, Mistral, Hugging Face) 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, Sage Maker, 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, Hugging Face Transformers).

Experience with agentic frameworks (Lang Chain Agents, Lang Graph, CrewAI, Auto Gen).
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, Bento

ML, Ray Serve.