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.