
Artificial Intelligence Engineer
3 weeks ago
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 DoAs 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.
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).
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