Twinamics

Company Description Twinamics is building the Autonomous Brain for Enterprises. We transform messy, siloed data into real-time intelligence and autonomous actions—helping businesses grow revenue without growing headcount. At the core of Twinamics is our proprietary DIPPCAA Engine (Data → Insight → Prediction → Prescription → Command → Action → Adaptation). It powers an end-to-end Data-to-Action Infrastructure that connects data, reasons over it, and executes business-critical decisions through AI Employees (AI-CXOs and their Agent Workforce). If you’re excited to work at the frontier of agentic AI, orchestration frameworks, and enterprise-scale automation —this role is for you. What You’ll Do As an AI Engineer at Twinamics , you’ll: Design and implement agentic AI systems with stateful reasoning, memory, and orchestration. Develop Digital Twin AI Employees (e.g., Finance CXO, Sales Lead) using LLMs, SLMs, and custom orchestration logic. Integrate AI agents with enterprise systems (ERP, CRM, WhatsApp, Accounting APIs, Voice Platforms). Build plug-and-play AI templates for cross-industry use cases (hospitality, manufacturing, supply chain, logistics). Optimize for scalability: retries, fail-safes, state persistence, and performance tuning. Collaborate with product designers and developers to bring enterprise-ready AI infra to life. Skillsets We’re Looking For Core AI & ML Strong background in Machine Learning and Deep Learning (esp. time-series & sequential modeling). Experience with LLMs, SLMs, or hybrid architectures for reasoning + prediction. Knowledge of probabilistic modeling (Bayesian methods, Monte Carlo, Markov Decision Processes). Experience in time-series forecasting (ARIMA, Prophet, RNN/LSTM/GRU, Transformer-based models). Familiarity with anomaly detection techniques to capture unexpected signals. Understanding of multi-signal fusion (internal + external data streams). Strong grasp of causal inference & correlation vs. causation for accurate event detection. Prescriptive AI & Decisioning Ability to move from prediction → prescription (recommend optimal actions, not just forecasts). Knowledge of reinforcement learning, optimization algorithms, or decision theory . Familiarity with control systems for closed-loop feedback in enterprise workflows. Data Layer & Infra Hands-on with data pipelines (ETL/ELT, Apache Kafka, Airflow, dbt, or similar). Experience with vector databases (Pinecone, Weaviate, Milvus, pgvector) for memory/state management. Strong SQL + NoSQL experience (Postgres, Mongo, etc.) for structured/unstructured data. Data architecture skills: schema design, feature engineering, real-time + batch pipelines . Enterprise Integration Ability to connect models into ERP, CRM, Finance, and Supply Chain systems . Strong API design & integration skills (REST, GraphQL, gRPC). Bonus Points Exposure to knowledge graphs or graph databases (Neo4j, TigerGraph) for event relationships. Familiarity with streaming data (IoT, sensor data, transaction logs) .