Job Title: Full Stack Developer (Python)
Location: Lucknow (onsite)
Experience: 3–5 years
Company: GC Cloud Info System PVT LTD
GC Cloud Info System PVT LTD fast-growing cloud solutions and software services company delivering scalable web and cloud-native applications to enterprise and SMB clients. We focus on modern architectures, microservices, and DevOps practices.
Role Overview-
We are seeking a skilled Full Stack Developer with 3–5 years of hands-on experience building backend services and responsive frontends. The ideal candidate is proficient in Python (FastAPI/Django), RESTful API design, relational and NoSQL databases, frontend frameworks, and cloud deployment. You will work across the stack to design, implement, test, and maintain production-grade applications.
Technical Requirements — Python, Servers & Libraries
- Deep Python backend expertise (3–5 years) with hands‑on experience building production services.
- Application server models:
- WSGI (synchronous): for traditional frameworks and blocking apps (e.g., Django synchronous views). Use when app is CPU/bounded or backward compatible with WSGI tooling.
- ASGI (async-capable): for async IO, websockets, high-concurrency APIs (e.g., FastAPI, Django Channels). Use for real‑time, long‑polling, or high-throughput services.
- Servers/runners: uvicorn (lightweight ASGI server, great for development and production with workers), gunicorn (WSGI server; use with sync Django or with uvicorn workers for ASGI), hypercorn, daphne — know when to pair (e.g., gunicorn + uvicorn workers) and how to configure workers, timeouts, and graceful shutdown.
- Common patterns: synchronous vs asynchronous view/handler design, event loop considerations, connection pooling, and CPU-bound task offloading (Celery/RQ/BackgroundTasks).
- ORMs & DB access:
- Relational ORMs: SQLAlchemy (Core & ORM), Django ORM, Peewee.
- Async ORMs: Tortoise-ORM, SQLModel (with async SQLAlchemy), async SQLAlchemy usage patterns.
- Migrations: Alembic, Django migrations.
- Query optimization, indexing, transactions, connection pooling (psycopg2/asyncpg).
- Vector DBs & embeddings:
- Familiarity with vector search and integration: FAISS (library), Milvus, Weaviate, Pinecone, Qdrant.
- Knowledge of embedding pipelines (sentence-transformers, OpenAI embeddings) and building/maintaining indexes, similarity search, and hybrid search (vector + metadata filters).
- ML / Computer Vision (initial/working experience expected):
- Face recognition basics: OpenCV, dlib, face_recognition (based on dlib), InsightFace, familiarity with embeddings, face detection vs recognition, alignment, thresholds, and privacy/security considerations.
- NLP, Topic Clustering & NER:
- Core NLP/transformer libraries: Hugging Face Transformers, spaCy (including spaCy pipelines and training NER), NLTK, Gensim.
- Topic modeling/clustering: classical (LDA via Gensim), embedding‑based approaches (BERTopic, clustering embeddings with HDBSCAN/KMeans), dimensionality reduction (UMAP/t-SNE).
- Named Entity Recognition (NER): spaCy, Transformers-based NER, fine-tuning strategies, evaluation metrics.
- Embeddings & pipelines: sentence-transformers, tokenization, batching, GPU acceleration for inference, handling long documents (chunking/aggregation).
- Testing, observability & performance:
- Unit/integration testing (pytest), API contract tests, load testing concepts.
- Instrumentation: logging, Prometheus metrics, distributed tracing (OpenTelemetry), structured logs.
- Security & deployment:
- Auth patterns (OAuth2, JWT), rate limiting, CORS, input validation (pydantic), secure storage of secrets.
- Containerization and orchestration: Docker, Kubernetes basics; CI/CD pipelines for model/code deployments.
- Practical experience applying these to product features: building REST/GraphQL endpoints, async background jobs, real‑time websockets, and integrating ML/NLP components into production pipelines.
Plus — Full Stack Universal Skills (desirable)
- Frontend: React (preferred), Vue or Angular; strong JS/TypeScript, responsive UI, component-driven design.
- Styling: CSS/Sass, Tailwind, Bootstrap, accessibility best practices.
- API design: RESTful patterns, versioning, OpenAPI/Swagger; familiarity with GraphQL is a plus.
- Testing: frontend testing (Jest/RTL), E2E testing (Cypress).
- DevOps & infra: Git workflows, GitHub Actions/GitLab CI/Jenkins, IaC basics (Terraform/CloudFormation), CI/CD for ML models.
- Databases & caching: PostgreSQL/MySQL, Redis, message queues (RabbitMQ/Kafka), backup/DR awareness.
- Monitoring & SRE basics: Prometheus/Grafana, alerts, SLO/SLI awareness.
- Soft skills: code reviews, design/architecture discussions, Agile/Scrum collaboration, clear technical documentation.
- Optional but valuable: serverless (AWS Lambda), experience with SaaS/cloud-native product development, familiarity with data privacy/compliance (GDPR).
Preferred Qualifications
- Experience with microservices architecture and event-driven systems (Kafka, flask, RabbitMQ).
- Knowledge of serverless technologies (AWS Lambda, Azure Functions).
- Familiarity with infrastructure-as-code (Terraform, CloudFormation).
- Experience with testing frameworks (pytest, frontend testing tools).
- Prior work in cloud-native SaaS products.
Pay: ₹30,000.00 - ₹55,000.00 per month
Work Location: In person
