ML Engineer II

AI/ML Full-Stack Developer Role Overview We are looking for an AI/ML Full-Stack Developer who can build reusable solutions combining cloud data platforms, machine learning, and RAG-based conversational AI. The developer should be comfortable working end-to-end—from data extraction and transformation through AI/ML processing to executive-facing dashboards and chat interfaces. Must-Have Skills Area Requirement Cloud & Data Hands-on experience with GCP, AWS, or Azure, including cloud data querying, transformation, and integration Databricks Experience developing and managing Databricks data pipelines SQL Strong SQL skills for complex querying, transformation, aggregation, and analytics ML / Analytics Experience applying ML techniques for trend analysis, anomaly detection, forecasting, and data visualization RAG Ability to build RAG solutions from scratch, including multi-source data ingestion, retrieval, context construction, and response generation LLM Integration Experience integrating LLMs through APIs and managing prompts, context windows, token consumption, latency, and cost Business Analytics Ability to translate technical/data outputs into business KPIs, metrics, insights, and executive-level dashboards End-to-End Development Ability to independently work across frontend, backend/API, data, AI/ML, and deployment layers Good-to-Have • Strong React.js experience building interactive dashboards and implementing role/user-based access • Experience across multiple business domains such as healthcare, finance, retail, or insurance • Vector databases: Pinecone, Weaviate, pgvector • Docker and CI/CD • LangChain, LlamaIndex, or other agentic/AI frameworks • BI platforms: Power BI, Looker, Tableau • Experience with AI/ML governance and responsible AI practices • Compliance awareness, particularly HIPAA, GDPR, or equivalent regulations • Experience building reusable/reference architectures and accelerators Expected Solution Architecture Skills The candidate should ideally be able to develop a solution along the following flow: Cloud/Data Sources → Databricks/Data Pipelines → SQL/Data Layer → ML/AI Processing → RAG/LLM Layer → APIs → React Dashboard + Chatbot They should understand how to connect these layers rather than being limited to a single technology stack. Key Deliverables The developer should be capable of building: 1. Executive dashboards with KPI, trend, anomaly, and drill-down views. 2. Role-based dashboards with appropriate data and feature access. 3. RAG chatbot capable of retrieving information from multiple structured and unstructured sources. 4. AI/ML insight engine for trends, anomalies, patterns, and recommendations. 5. LLM integration layer with token, latency, and cost optimization. 6. Reusable components/frameworks that can be adapted across different projects and domains. 7. Cloud-native data pipelines using Databricks and cloud services.