AI/ML Engineer

1 day ago

Coimbatore, Tamil Nadu, India a3-935f-4e00-86ec-0bb85aeca5b8 Full time ₹ 12,00,000 - ₹ 25,00,000 per year

Build an autonomous AI agent system that combines real-time data processing, multi-agent LLM orchestration, and advanced ML research. You'll work directly with the founder to transform a working data pipeline into an intelligent, self-improving system that analyzes markets using cutting-edge AI.

Current State:

  • Real-time data ingestion operational (100K+ ticks/sec via QuestDB + Redis)
  • Multi-source data feeds integrated (Databento, ThetaData, Benzinga, Unusual Whales, FRED)
  • Agent orchestration framework designed (Google ADK, LangGraph)
  • Basic alerting system functioning

Your Mission: Build the intelligence layer that turns raw data streams into actionable insights using ML, knowledge graphs, and LLM-powered agents.

What You'll Build

Feature Engineering & Retrieval System

  • Design compressed data representations for efficient LLM retrieval
  • Build knowledge graph (Neo4j) linking events patterns outcomes
  • Create RAG system: embed patterns, enable semantic retrieval of historical analogues
  • Develop signal scoring engine combining multiple data streams

AI Agent Implementation

  • Build Research Agent: LLM-driven hypothesis generator discovering patterns in data
  • Implement Monitoring Agent: Real-time anomaly detection and alerting
  • Create Analysis Agent: Sentiment and narrative extraction using FinBERT + LLMs
  • Deploy Shadow Mode: Run agents on live data, validate against outcomes

Strategy & Automation

  • Develop pattern detection using ML (matrix profile/STOMP for motif discovery)
  • Build backtesting framework with walk-forward optimization
  • Implement execution automation with optimization algorithms
  • Create self-improvement loop: agents learn from outcomes, adapt strategies

Required Skills

Core Technical:

  • Expert Python for production systems (not just notebooks)
  • ML fundamentals: scikit-learn, PyTorch, embeddings, retrieval systems
  • Time-series expertise: statistical analysis, pattern recognition, forecasting
  • Systems design: Can architect data pipelines and production ML systems
  • Data engineering: Experience with high-volume streaming data

Quantitative Analysis:

  • Strong statistical analysis and hypothesis testing
  • Experience with time-series data and signal processing
  • Understanding of optimization algorithms and search spaces
  • Ability to validate models rigorously and avoid overfitting

Preferred Skills

  • LangChain/LangGraph or similar LLM orchestration frameworks
  • Graph databases (Neo4j) or knowledge graph work
  • Vector search (pgvector, Qdrant) and RAG architectures
  • Real-time systems: Kafka, Redis Streams, QuestDB
  • Deep learning: LSTM, Transformers for sequential data
  • Academic background: MS/PhD in CS, Physics, Math, Statistics, Data Science

Tech Stack

Data Infrastructure:

  • QuestDB (time-series DB) + Redis Streams for real-time ingestion
  • PostgreSQL for analytics, Neo4j for knowledge graphs
  • Multiple API integrations for diverse data sources

AI/ML Stack:

  • LangChain + LangGraph for agent workflows
  • Google ADK for multi-agent orchestration
  • PyTorch for deep learning, scikit-learn for classical ML
  • HuggingFace Transformers (sentiment, forecasting models)
  • Claude, GPT. Gemini, Grok for LLM capabilities

Infrastructure:

  • Python 3.11+ with async patterns
  • Docker + systemd orchestration
  • GitHub Actions CI/CD

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