AI Full Stack Engineer

Job Description 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 .

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: 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.

Key Domain/ Technical Skills DevOps: CICD, Kubernetes/Openshift, Docker, Jenkins, Bash Backend: Python, SQL, Celery, Gen AI prompting Frontend: TypeScript, HTML, Jinja