About the role
You'll build the AI and machine learning systems that power Matilda Cloud's intelligent cloud cost optimisation, policy management, and recommendation engine. This means shipping production agentic workflows, RAG pipelines, fine-tuned models, and ML services — all running across AWS, Azure, GCP, and OCI at enterprise scale.
What you'll work on
Migrate cloud pricing and policy APIs to agentic AI architectures using LangGraph and MCP
Build RAG pipelines for AI-generated cost allocation and capacity reports
Design and execute the distillation pipeline: teacher data capture (Claude/Bedrock) → SFT/DPO training → Qwen student model
Build the cloud cost forecasting engine and anomaly detection service (Prometheus + time-series ML)
Instrument LLMOps observability: tracing, cost monitoring, groundedness evals (LangSmith)
Expose Matilda's cloud discovery data as MCP tool surfaces for agentic consumption
Must have
4+ years of software engineering with Python as primary language
Hands-on with LLM APIs — AWS Bedrock, OpenAI,



or Anthropic — including function calling and structured outputs
Built at least one production RAG pipeline (chunking, vector DB, hybrid retrieval)
LangChain or LangGraph experience — agent loops, tool use, state management
Robust cloud experience on at least two of: AWS, Azure, GCP, OCI — specifically AI/ML services (Bedrock, SageMaker, Azure ML, Vertex AI)
Kubernetes for deploying and scaling ML services
Experience with ML data pipelines — feature engineering, ETL for training data
Time-series analysis or anomaly detection in a production setting
Nice to have
MCP (Model Context Protocol) — building or consuming MCP servers
LLM fine-tuning: SFT dataset construction, DPO, or LoRA/QLoRA
Model distillation — teacher/student patterns
LangSmith or equivalent LLMOps observability platform
FinOps / cloud cost domain knowledge (a big plus given our product)
Experience with LiteLLM, model routers, or multi-provider LLM setups

📌 Genetic AI Engineer (Hyderabad)
🏢 Matilda Cloud
📍 Hyderabad