AI Full Stack Engineer Developer
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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.