Full Stack Developer (Expert) 1508

Must have tech stack - 10 years and above;

Java

Enterprise Java Development

Spring / Spring Boot

REST API Development

Microservices Architecture

Integration Services

Python

• 8 years Python

Data processing

Automation

APIs

AI integrations

AI Engineering &

• GenAI

LLMs

RAG

Prompt Engineering

AI Agents

• AI-assisted Development

Software Engineering

Enterprise application development

• Object-oriented design

System design

Secure coding

Production support

API &

• Integration Development

REST APIs

Service Integration

API Design

• Event-driven architecture

Data Engineering / Data Processing

ETL / ELT

Data Pipelines

Data Transformation

Database Development

Data Quality

Databases

PostgreSQL

Oracle

SQL Development

Role &

• Responsibilities

• Design, build, test, deploy, and operate secure, scalable, production-grade software and data products.
• Develop high-quality Python and Java solutions for APIs, microservices, automation, integrations, and platform services.
• Engineer reliable batch and event-driven data pipelines with strong observability, governance, and data quality controls.

Build AI-enabled solutions leveraging:

LLMs

AI Agents

• RAG (Retrieval-Augmented Generation)
• Prompt &
  • Context Engineering

  AI Evaluation Frameworks

• Guardrails &
  • Human Approval Workflows

Drive engineering excellence through:

CI/CD

• Infrastructure as Code (Terraform preferred)

Automated Testing

Secure Coding Practices

Code Reviews

Production Readiness

• Collaboration closely with Product Owners, Architects, Data Engineers, Security, and Operations teams.
• Mentor engineers and contribute to technical leadership, architecture, and best practices.

Qualifications

• Relevant degree or diploma in Computer Science, Software Engineering, Information Technology, Data
• Engineering or a related field.
• 10 years of relevant software engineering experience, including responsibility for complex production systems.
• Expert-level delivery using Python and/or Java, with strong data and integration engineering experience.
• Practical evidence of AI-assisted or AI-native engineering, including evaluation, governance and accountable human review.
• AWS, Java, Python, data engineering, architecture, DevOps, Kubernetes, Terraform, Agile or AI-related certifications are advantageous.