9 Polars Jobs

As a Backend Developer, your primary responsibility will be to develop and maintain backend services using Python, with a strong emphasis on FastAPI, NumPy, and Polars for efficient handling of data. You will be tasked with building and managing agentic workflows utilizing LangChain, LangGraph, and MCP Agents to facilitate dynamic, multi-step reasoning. Additionally, you will design and execute RAG pipelines for contextual information retrieval and response generation. Integration and optimization of MongoDB or similar vector databases like FAISS and Pinecone for semantic search and embedding storage will also fall under your purview. Collaboration with cross-functional teams to deploy scalable AI services in production will be a crucial part of your role. Furthermore, you will be responsible for conducting performance tuning, testing, and deployment of AI components, while also keeping abreast of the latest developments in GenAI, LLMs, and agentic architectures. The ideal candidate for this position should possess a minimum of 3-6 years of experience in backend development using Python. You should have hands-on experience with FastAPI for constructing RESTful APIs, as well as proficiency in NumPy and Polars for numerical and tabular data processing. A solid understanding of Generative AI concepts is essential, along with practical experience in working with LangChain, LangGraph, and MCP Agents. Experience in building and deploying agentic RAG systems and familiarity with MongoDB or other vector databases for semantic search and retrieval will be advantageous. Knowledge of cloud platforms such as Azure and containerization tools like Docker/Kubernetes will be considered a plus. To qualify for this role, you should hold a Bachelors or Masters degree in computer science, data science, mathematics, or a related field.,

Job Title: Python Developer (38 Years Experience) Location: Chennai Job Type: Full-Time Work Mode: Office (5 Days) Experience Required: 3 to 8 Years Job Description Ability to perform data transformations using Python, pandas, numpy, polars. Ability to query data from SQL and NoSQL databases. For SQL: Given a database schema should be able to create a query to extract desired data. For NoSQL: Experience performing hierarchical queries in REST or experience with GraphQL. Database operation (indexing etc), cache (redis etc), data management, handle missing data etc. Data visualization in Python (Plotly, Bokeh, matplotlib etc.) Some experience with distributed programming using Spark or Dask or similar toolkits that can take sequential code and run it across multiple processes. Familiar with machine learning, experience with deep learning model analysis, popular architectures, augmentations, fine tune, boosting, dimension reduction etc. Basic knowledge of computer vision and image processing, understand concept of feature extraction, object detection and classification, segmentation, denoise, super resolution etc. Familiar with python / tensorflow / pytorch / openCV etc. Good software skill, knows concept and object-oriented design, and basic software system design Experience of organize and aggregate large datasets Good data analysis and analytic skill

Backend Developer Database PostgreSQL PostGIS:For all PostgreSQLpsycopg2 asyncpg PostgreSQL adapters PythonGDAL OGR via osgeo module T Pandas, PAWS S3/RDS/EC2 olars tabular data manipulation, NumPy Django and Django REST GeoJSON, ShapefileGeoTIFF

Good knowledge of building Data Pipelines using Python with Flask API and library Polars and strong knowledge of relational databases like MySQL, DB2 and Mssql server and non relational databases MongoDB. Knowledge of Kafka is add on. Stack: 1. Python 2. Polars 3. MySQL, DB2 and Mssql server databases 4. MongoDB. 5. Very Good Communication skills. 6. Open to travel to USA

Good knowledge of building Data Pipelines using Python with Flask API and library Polars and strong knowledge of relational databases like MySQL, DB2 and Mssql server and non relational databases MongoDB. Knowledge of Kafka is add on. Stack: 1. Python 2. Polars 3. MySQL, DB2 and Mssql server databases 4. MongoDB. 5. Very Good Communication skills. 6. Open to travel to USA

Job Responsibilities: To transition legacy Rules from Python using the Polars Library to SparkSQL To create new Rules using SparkSQL based on written requirements. Must Have Skills: Understanding of Polars library Understanding of SparkSQL (this is more important than Polars) Good English communication Ability to work in a collaborative environment Experience with US healthcare data preferred Understanding of SparkSQL (this is more important than Polars)

Skills -Pydantic, Python, SQl, Fast API Mail your resume with salary details: current CTC: Expected CTC: Notice Period: cynthia.s@noblq.com whatsapp -9677090220

Strong in Python and experience with Jupyter notebooks, Python packages like polars, pandas, numpy, scikit-learn, matplotlib, etc. Must have: Experience with machine learning lifecycle, including data preparation, training, evaluation, and deployment Must have: Hands-on experience with GCP services for ML & data science Must have: Experience with Vector Search and Hybrid Search techniques Must have: Experience with embeddings generation using models like BERT, Sentence Transformers, or custom models Must have: Experience in embedding indexing and retrieval (e.g., Elastic, FAISS, ScaNN, Annoy) Must have: Experience with LLMs and use cases like RAG (Retrieval-Augmented Generation) Must have: Understanding of semantic vs lexical search paradigms Must have: Experience with Learning to Rank (LTR) techniques and libraries (e.g., XGBoost, LightGBM with LTR support) Should be proficient in SQL and BigQuery for analytics and feature generation Should have experience with Dataproc clusters for distributed data processing using Apache Spark or PySpark Should have experience deploying models and services using Vertex AI, Cloud Run, or Cloud Functions Should be comfortable working with BM25 ranking (via Elasticsearch or OpenSearch) and blending with vector-based approaches Good to have: Familiarity with Vertex AI Matching Engine for scalable vector retrieval Good to have: Familiarity with TensorFlow Hub, Hugging Face, or other model repositories Good to have: Experience with prompt engineering, context windowing, and embedding optimization for LLM-based systems Should understand how to build end-to-end ML pipelines for search and ranking applications Must have: Awareness of evaluation metrics for search relevance (e.g., precision@k, recall, nDCG, MRR) Should have exposure to CI/CD pipelines and model versioning practices GCP Tools Experience: ML & AI: Vertex AI, Vertex AI Matching Engine, AutoML, AI Platform Storage: BigQuery, Cloud Storage, Firestore Ingestion: Pub/Sub, Cloud Functions, Cloud Run Search: Vector Databases (e.g., Matching Engine, Qdrant on GKE), Elasticsearch/OpenSearch Compute: Cloud Run, Cloud Functions, Vertex Pipelines, Cloud Dataproc (Spark/PySpark) CI/CD & IaC: GitLab/GitHub Actions Location: Remote- Bengaluru,Hyderabad,Delhi / NCR,Chennai,Pune,Kolkata,Ahmedabad,Mumbai

Expert in Python, with knowledge of at least one Python web framework such as Flask, FAST, Django etc. Knowledge and experience on using Python libraries like Numpy, Pandas, Polars, Dask. Familiarity with some ORM (Object Relational Mapper) libraries. Familiarity with Relational and/or No-SQL databases. Proficient in using editors like VS Code, Eclipse, PyCharm, PyScripter etc. Understanding of fundamental design principles behind a scalable application. Strong unit test and debugging skills. Proficient understanding of code versioning tools such as Git, Mercurial or SVN. Basic understanding of front-end technologies, such as JavaScript, HTML5, and CSS3. Knowledge of Kafka would be a huge plus.