Python Developer - (Python + AI)

Roles and Responsibilities:

Work Mode: Hybrid / Remote

Role Overview

We are looking for a Python Developer with 3 to 5 years of experience to join an AI-first, cross-functional delivery pod. The role involves building production-grade Python services and platforms from scratch, integrating internal and external applications with Agentic AI platforms, and working with databases end to end.

The ideal candidate is disciplined about code quality strong in unit testing, debugging, and refactoring — and is comfortable delivering in fast-paced, time-boxed Agile sprints. Experience with human-in-the-loop (HITL) evaluation and first-time-right engineering practices is essential.

Key Responsibilities

• Platform and Backend Development
• Design and build Python-based backend services and APIs (FastAPI, Flask, or Django REST Framework) from scratch through to production deployment.
• Write clean, modular, and reusable code following SOLID principles and agreed engineering standards.
• Design and manage relational databases (PostgreSQL, MySQL) and NoSQL stores (MongoDB, Redis), including schema design, migrations, and query optimisation.
• Build and maintain RESTful and GraphQL APIs with clear OpenAPI or Swagger documentation.
• Participate in system design discussions and translate requirements into well-scoped technical tasks.

Agentic AI Integration

• Integrate Agentic AI platform components — LLM-based agents, tool-use pipelines, and orchestration layers — into Python services.
• Build agent workflows using frameworks such as LangChain, LangGraph, CrewAI, or AutoGen, connecting agents to internal tools, APIs, and databases.
• Develop and maintain RAG (Retrieval-Augmented Generation) pipelines including embedding workflows and vector database integration.
• Build prompt pipelines with structured input/output validation using Pydantic or equivalent schema enforcement.
• Apply HITL (human-in-the-loop) evaluation patterns to review, validate, and refine AI agent outputs before they reach end users or downstream systems.
• Integration with Internal and External Systems
• Integrate Python services with internal enterprise applications via secure, well-documented API contracts.
• Connect to external third-party APIs and SaaS platforms, handling authentication flows (OAuth2, JWT, API keys), retries, and error propagation.
• Build async workflows using message queues (Celery, RabbitMQ) or task runners where applicable.
• Ensure integration reliability through retry logic, circuit breakers, and monitoring hooks.
• Code Quality and Engineering Discipline
• Write comprehensive unit tests and integration tests using pytest, targeting meaningful coverage as a baseline — not an afterthought.
• Follow first-time-right principles: invest in design clarity and edge-case handling upfront to reduce rework in later sprints.
• Debug complex issues across the stack — analyse logs, traces, and metrics to locate root causes and resolve with minimal escalation.
• Regularly refactor existing code for clarity, performance, and maintainability; flag and manage technical debt during sprint planning.
• Participate in code reviews — give and receive feedback constructively and consistently.
• Agile Delivery and Collaboration
• Work confidently in time-boxed Agile sprints — estimate accurately, commit deliberately, and communicate blockers early.
• Collaborate within a cross-functional pod with front-end developers, QA engineers, and AI/ML specialists.
• Break down epics into well-scoped stories and tasks; keep sprint boards and issue trackers accurate and up to date.
• Participate in daily standups, sprint retrospectives, and backlog refinement sessions.
• Support QA handoff with clear test cases, environment notes, and API contract documentation.

Required Skills and Qualifications

• Core Python and Backend
• 3 to 5 years of professional Python development experience in production environments.
• Proficient with at least one Python web framework: FastAPI (preferred), Flask, or Django REST Framework.
• Strong understanding of async programming (asyncio, HTTPX), dependency injection, and middleware patterns.
• Hands-on experience with Pydantic for data validation and schema design.
• Solid command of Git-based workflows: feature branching, pull requests, and commit hygiene.
• Databases
• Hands-on experience with PostgreSQL or MySQL: schema design, indexing, query plans, and migrations (Alembic).
• Working knowledge of MongoDB for document storage and Redis for caching and pub/sub.
• Awareness of vector database concepts (pgvector or cloud-hosted) for AI-adjacent applications.
• Agentic AI and LLM Integration
• Practical experience integrating LLM APIs (OpenAI, Anthropic Claude, or equivalent) into backend services.
• Exposure to at least one agent orchestration framework: LangChain, LangGraph, CrewAI, AutoGen, or similar.
• Understanding of RAG patterns, embedding pipelines, and prompt chaining with structured output validation.
• Familiarity with HITL workflows: checkpointing, human review loops, confidence thresholds, and override mechanisms.
• Quality Engineering
• Test-alongside or test-driven development practice using pytest — including mocking, fixtures, and parametrized tests.
• Comfortable with debugging tools: pdb/ipdb, log analysis, and profiling.
• Experience with CI/CD pipelines (GitHub Actions, GitLab CI, or Jenkins) including automated test gates.

Agile and Collaboration

• Experience working in 2-week Agile sprints within a cross-functional pod.
• Clear technical communicator — able to document APIs, write decision records, and explain design choices to non-engineers.
• Self-driven with a track record of delivering independently scoped work within sprint commitments.

Nice to Have

• Basic front-end knowledge (React or Vue) — enough to debug and unblock integration issues.
• Experience with Docker and Kubernetes basics for local or staging deployments.
• Familiarity with cloud platforms (AWS, Azure, or GCP) for serverless Python workloads.
• Knowledge of event-driven architectures using Kafka or RabbitMQ.
• Exposure to observability stacks such as Prometheus, Grafana, Datadog, or ELK.
• Experience in a regulated domain such as pharma, fintech, or healthcare.

What We Offer

• Opportunity to work on real Agentic AI products in an engineering-first culture.
• Competitive compensation
• Flexible hybrid or remote working with a high-trust, outcome-focused team culture.