Software Engineer for ML Infrastructure at Slack, architecting systems to support large scale AI deployment and reliability. Engage in deep systems engineering focusing on ML lifecycle and infrastructure scalability.
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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