We are seeking a Senior Python Developer (Data Science) with a Software Engineer First mindset to join our core engineering team.
In this role, you won't just train ML models in notebooks—you will architect, build, and deploy high-performance FastAPI microservices, asynchronous data pipelines, and scalable NLP/LLM solutions into production cloud environments (AWS).
If you love writing clean, asynchronous Python code, optimizing REST APIs, and integrating cutting-edge NLP/GenAI tools into robust backend architectures, we want to hear from you!
Key Responsibilities
- Backend Microservices: Design, build, and maintain production-ready REST APIs and asynchronous microservices using FastAPI (with async/await, Pydantic, and SQLAlchemy).
- Data Engineering & ETL: Build scalable, high-throughput data processing pipelines handling structured and semi-structured data (JSON, Parquet, CSV, SQL) using Pandas and PySpark.
- NLP & Model Integration: Integrate advanced NLP models, Transformers, LLMs, and RAG architectures (SpaCy, LangChain, Transformers, Rasa) directly into backend services.
- MLOps & Deployment: Containerize applications using Docker and deploy ML endpoints via AWS SageMaker, AWS Lambda, and container registries (ECR).
- Code Quality & Testing: Write clean, modular, object-oriented Python (OOP) with comprehensive unit/integration test suites using pytest (>85% coverage) and CI/CD pipelines.
Mandatory Qualifications (Must-Haves)
- 6+ years of professional software development experience in Python.
- Proven track record building and deploying production FastAPI microservices and RESTful APIs.
- Deep experience in data manipulation and ETL using Pandas, SQL, and Parquet/JSON formats.
- Hands-on experience integrating NLP / Machine Learning frameworks (e.g., SpaCy, Transformers, LangChain, RAG, Rasa) into backend applications.
- Production experience with AWS SageMaker (model hosting/endpoints) and Docker containerization.
- Strong grasp of asynchronous programming (asyncio, Celery/Redis), design patterns, and testing with pytest.
Nice-to-Haves
- AWS Certifications (Solutions Architect or Machine Learning Specialty).
- Experience with Vector Databases (Qdrant, Pinecone, FAISS) or Multi-Agent frameworks (LangGraph, CrewAI).
- Experience with message brokers like Apache Kafka or RabbitMQ.
