Artificial Intelligence Engineer

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).

agentic AI, orchestration frameworks, and enterprise-scale automation

AI Engineer at Twinamics

• 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)

  .