Python Software Engineer

Job Overview

The Applications Development Analyst position is responsible for participating in the development, testing, implementation, and maintenance of application systems and microservices in coordination with the broader engineering team. The overall objective of this role is to contribute to technical design, programming, system analysis, and automated deployments in a fast-paced agile environment.

The ideal candidate is a versatile and adaptable engineer who thrives across evolving technical domains-spanning backend microservices, full-stack modules, data workflows, and AI integrations-with a strong appetite for continuous learning and problem-solving.

Key Responsibilities

• Microservices Development: Participate in the design, development, unit testing, and maintenance of Python-based microservices and RESTful APIs using modern frameworks (FastAPI / Flask).
• Coding & Implementation: Write clean, modular, and maintainable core Python code adhering to standard software engineering best practices, design patterns, and architectural guidelines.
• Database & Data Layer Design: Implement and maintain robust data persistence layers utilizing document and NoSQL databases such as MongoDB and Couchbase, ensuring efficient indexing, schema design, and query performance.
• Containerization & Deployment: Containerize services using Docker and assist in managing, configuring, and deploying workloads onto Kubernetes / OpenShift clusters.
• CI/CD Integration: Work with automated CI/CD pipelines and deployment tools (e.g., Helm, Harness) for automated building, linting, testing, and continuous deployment.
• Full-Stack & GenAI Integration: Build and integrate frontend interfaces with Angular where required, and assist in connecting LLM / Generative AI capabilities into backend workflows.
• Semantic & Data Modeling: Assist in building and integrating semantic models, embeddings, metadata definitions, and structured schemas to support intelligent data retrieval, semantic search, and domain data layers.
• Cross-Functional Flexibility: Demonstrate agility by adapting to varying technical demands across backend, UI, data engineering, and emerging AI technologies as project needs evolve.
• Agile Participation: Actively engage in daily standups, sprint planning, task estimation, code reviews, and retrospectives within an Agile/Scrum team.
• Issue Resolution & Support: Assist in troubleshooting system defects, investigating application errors, analyzing logs, and supporting production releases.
• Risk & Control: Appropriately assess risk when technical decisions are made, adhering to company security standards, coding guidelines, and regulatory compliance requirements.

Required Qualifications & Technical Skills

1. Core Technical Experience

• Core Python Proficiency: Strong foundation in Python, data structures, OOP concepts, functional paradigms, error handling, and asynchronous programming (asyncio).
• Microservices Architecture & APIs: Practical experience in designing and developing decoupled, resilient microservices and RESTful APIs using FastAPI or Flask, including inter-service communication, error handling, and API versioning.
• NoSQL Databases (MongoDB & Couchbase): Solid hands-on experience working with document/NoSQL stores such as MongoDB and Couchbase, including document modeling, query optimization, indexing strategies, and connection lifecycle management.
• Containers & Kubernetes: Hands-on experience building Docker containers and deploying/running services in Kubernetes (understanding Pods, Deployments, Services, ConfigMaps, Secrets).
• CI/CD & Version Control: Good working knowledge of Git workflows (branching, PRs/code reviews) and continuous integration/continuous deployment pipelines (Helm, Harness).

Preferred & Key Add-on Skills

1. Frontend Development (Key Add-on)

• Working knowledge or hands-on experience with Angular (v12+), TypeScript, RxJS, HTML5, and CSS/SCSS to support full-stack feature delivery.

2. LLM, GenAI & Semantic Modeling (Key Add-on)

• Understanding of Generative AI principles, prompt engineering, and LLM APIs (e.g., OpenAI, Claude, Gemini).
• Basic exposure to orchestration tools (Agent Development Kit / ADK, LangChain) and Retrieval-Augmented Generation (RAG) concepts.
• Understanding of semantic models, vector embeddings, knowledge representations, or data semantic layers for structured/unstructured search and AI grounding.

3. Data Engineering (Additional Advantage)

• Familiarity with data engineering concepts, batch/stream processing, and messaging queues (e.g., Kafka).
• Exposure to big data processing with Apache Spark and strong proficiency in SQL (relational database queries and optimization).

Professional & Behavioral Competencies

• Technical Flexibility & Adaptability: High adaptability and willingness to take on diverse tasks across different layers of the technology stack (backend, frontend, data, and AI/ML).
• Fast-Paced Delivery: Ability to manage shifting priorities, learn new technologies rapidly, and deliver quality results in a fast-paced environment.
• Problem Solving: Good analytical and debugging mindset with attention to detail.
• Communication & Collaboration: Clear verbal and written communication skills; effective team player capable of working cross-functionally across distributed teams.

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