AI Full Stack Engineer Developer
Posted Updated
Description
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.
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.