Senior Machine Learning Engineer at Bumble developing scalable AI systems for personalized user interactions. Leading machine learning model development and deployment from exploration to production.
About the role
- Architect designing enterprise-grade AI/ML architectures for Quantiphi. Leading AI applications and ML strategy with a focus on scalability, security, and integration.
Responsibilities
- 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
- 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
- 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
- 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
Requirements
- 9+ Years Work experience
- 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
Benefits
- 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
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
No Education Requirement
Tech skills
Location requirements
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