Artificial Intelligence Engineer

3 days ago

Varanasi, Uttar Pradesh, India Twinamics Full time

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).

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