AI Full Stack Engineer Developer

Experience: 6+ Years
Role: AI Full Stack Engineer Developer

Key Skills:
· DevOps: CICD, Kubernetes/Openshift, Docker,Jenkins, Bash

· Backend: Python, SQL, Celery, Gen AI prompting

· Frontend: TypeScript, HTML, Jinja

Responsibilities:

We are looking for a AI Full Stack Engineer who operates effectively at the intersection of system analysis, enterprise integration and data engineering to support GenAI initiatives.

The successful candidate will:

· Understand business and functional requirements

· Translate them into data flows and integration designs

· Work across upstream and downstream systems

· Ensure reliable movement, transformation and availability of data for GenAI use cases

· Develop scripts / programs to get data from different integration systems

· Develop APIs to integrated with relevant systems.

The focus is on getting data into the right place, in the right format, reliably.

What Matters Most

 Strong system thinking and ability to understand end to end flows

 Ability to challenge bad designs and propose better ones

 Strong stakeholder communication

 Hands on enough to troubleshoot and implement, but not a pure coder

 Experience navigating messy enterprise ecosystems

Key Responsibilities
1. System Analysis & Design

• Analyse business/technical requirements and translate them into data flows and integration designs
• Work with upstream and downstream teams to define data contracts and interfaces
• Identify gaps, inefficiencies and risks in current data movement processes
• Propose pragmatic solutions balancing speed, quality and maintainability

2. Integration & Data Movement

• Design and implement data movement across systems using:
• APIs
• SFTP and file based transfers
• Batch pipelines
• Coordinate integrations across systems in the DataLake ecosystem (Informatica, Cloudera, etc.)
• Ensure data is correctly transformed, mapped and delivered to target systems
• Troubleshoot integration issues across environments

3. Data Preparation for GenAI

• Support data ingestion and preparation for GenAI use cases:
• document ingestion
• data aggregation
• enrichment and transformation
• Work with structured and unstructured data
• Ensure data is usable for downstream AI workflows (RAG, search, investigation flows)

You are not asking them to build models, just make data usable for them.

4. Delivery & Coordination

• Work across multiple teams:
• data platforms
• application teams
• infrastructure
• security
• Support SIT, UAT and production rollouts
• Ensure integration reliability, error handling and monitoring
• Document flows, mappings and interfaces clearly

5. Production Support

· Provide production support for deployed applications, including incident investigation, troubleshooting, resolution, and service restoration.

· Perform bug fixes, code corrections, and application development changes required to resolve production issues and improve application stability.

· Develop and implement enhancements or technical changes arising from production issues, operational requirements, or continuous improvement.

· Troubleshoot issues across the full technology stack, including Python, Celery, database, Docker, Kubernetes/OpenShift, and CI/CD.

Requirements:

Key Requirements

Below are the key skillsets that will be required for all relevant tasks mentioned:

· 5-10 years of experience in system analysis, integration engineering, data engineering or technical delivery roles.

· Good SQL and Python skills for data handling, scripting, automation and troubleshooting.

· Good knowledge of front end stack, such as HTML, Jinja templating.

· Experience with Celery for asynchronous / background task processing

· Strong ability to translate requirements into system flows, data flows, interface specifications and implementation plans.

· Experience working with upstream and downstream teams to define and deliver enterprise integrations.

· Practical experience with REST APIs, SFTP, batch processing, file based integration and data pipeline orchestration.

· Good understanding of data mapping, transformation, aggregation, reconciliation and data quality controls.

· Exposure to Java

· Exposure to Cloudera or similar enterprise data platforms.

· Working knowledge of Git, branching, pull requests, code reviews and controlled release practices.

· Familiarity with CI/CD, Jira, Confluence and enterprise deployment processes.

· Experience of deploying and supporting application on Kubernetes and OpenShift (OCP)

· Experience with Control M or equivalent scheduling tools.

· Familiarity with logging and monitoring tools such as Splunk, Elastic Stack.

· Exposure to GenAI concepts such as document ingestion, RAG, embeddings and data preparation for AI workflows.

Strong communication skills, with the ability to challenge weak designs and coordinate across business, application, data, infrastructure and security teams.