AI & Data Engineer

In this role, you will contribute to the development of next-generation intelligent systems by architecting agentic workflows that can reason, remember, and act.

REQUIREMENTS

• Programming: Mastery of Python (FastAPI, Pydantic, Pandas, ScykitLeran, Pyspark).
• AI Frameworks: Hands‑on experience with LangChain, LangGraph, or similar libraries focused on agentic behavior. Understanding of MCP.
• Experience with Vector Databases (e.g., Pinecone, Milvus, Weaviate, or pgvector).
• Strong proficiency in Relational Databases (PostgreSQL / Snowflake / BigQuery).
• Data Warehousing: Clear understanding of Star/Snowflake schemas,Fact/Dimension modeling, and modern data stack principles.
• NLP: Proven experience with Hugging Face ecosystem, BERT-family models, andintent classification techniques.
• ETL: Able to create & maintain ETL jobs with Python & Airflow.

KEY RESPONSIBILITIES

1. AI & LLM Orchestration

• Agentic Systems: Design and implement autonomous agents capable of multi-step reasoning, planning, and tool use.
• State & Memory Management: Develop robust mechanisms for maintaining conversation context and long‑term memory in LLM workflows.
• MCP Integration: Implement the Model Context Protocol (MCP) to standardize how our AI models interact with external data sources, tools, and local environments.
• Retrieval Augmented Generation (RAG): Build and optimize RAG pipelines using vector databases to provide LLMs with relevant, real‑time context.

2. Data Engineering, Analytics & Machine Learning

• Analytical SQL: Write complex, high‑performance SQL for data analysis,transformation, and feature extraction.
• Data Modeling: Design and maintain Dimension and Fact tables within astructured Data Warehouse environment.
• Feature Engineering: Identify and extract key features from diverse datasets to improve model performance and business insights.
• Pipeline Development: Build scalable ETL/ELT processes that ensure data quality and availability for both AI models and BI tools.
• Ability to perform Exploratory Data Analysis (EDA) to inform model architecture.
• Supervised & Unsupervised Learning: Implement and evaluate models across the ML spectrum, from predictive regressions to discovery‑based clustering.
• Classification & Regression: Understanding of basic ML techniques to support data driven analytics.
• Recommendation Engines: Design and deploy recommendation systems(collaborative filtering, content‑based, or hybrid).
• Clustering: Use unsupervised techniques to identify patterns in dataset features and segment user behavior.

3. Natural Language Processing (NLP)

• Implement and fine‑tune NLP models for tasks such as Named Entity Recognition (NER) and Intent Translation.
• Work with transformer‑based architectures like BERT to enhance text understanding and classification within our product ecosystem.

PREFERRED QUALIFICATIONS

• 3–5 years of experience in related field
• Graph Databases: Experience with Neo4j or similar Graph DBs for advanced RAG(GraphRAG) and knowledge graph construction.
• DevOps/MLOps: Experience with Docker, Kubernetes, or CI/CD pipelines for AIservices.
• Protocol Knowledge: Deep familiarity with JSON‑RPC or the underlyingarchitecture of MCP.
• Knowledge of DBT.

Explore Our Benefits

We believe that anything can be realized. And we want to help you, make what you dream of come true. Become a part of our workforce!

We offer ongoing opportunities for your professional growth.

Health & Insurance

We provide employee health and employment insurance. We are committed to maintaining employee welfare, one of which is by providing catering menus for every employee.