Tech Development

gsk

Bengaluru 4 Years Exp Posted 1h ago

1. Data Pipeline & Platform Engineering

Independently design, build, deploy, and operate batch and streaming data pipelines that meet defined expectations for freshness, correctness, reliability, performance, and cost.
Own data models and datasets across ingestion, transformation, storage, and serving, using modern warehouse or Lakehouse technologies.
Monitor source and platform changes, including ServiceNow data exposed through Databricks views, and address inconsistencies that could affect downstream solutions or agent reliability.
Make informed technical decisions on pipeline, interface, data-model, and automation changes, escalating only where architectural, compliance, or business-risk thresholds require broader approval.
Control and monitor approved data access points across structured and unstructured sources, cloud platforms, documentation, and authoritative business repositories.

2. AI Agent & GenAI Engineering

Build, deploy, and enhance machine learning and GenAI solutions aligned with validated user requirements.
Develop and maintain AI agents using Python, Azure services, APIs, LLM frameworks, retrieval patterns, and appropriate interface technologies.
Use AI coding assistants and LLM tooling to plan, scaffold, refactor, test, document, and debug code, while validating outputs before adoption.
Create agent-based automations for data-quality investigation, failure triage, documentation, schema and lineage analysis, dataset discovery, and routine remediation.
Test, refine, and govern prompts; identify new prompts aligned with evolving business needs and approved solution scope.
Incorporate user feedback, agent metrics, and production outcomes into a closed-loop improvement process for prompts, models, and agent behavior.

3. Reliability, Observability & Trust Controls

Monitor system health, trace logs, response quality, hallucination indicators, data drift, failures, and operational trends.
Define and maintain practical detection thresholds, alerts, dashboards, and escalation paths for production issues.
Instrument pipelines and agents, respond to incidents, perform root-cause analysis, and drive corrective actions for reliability, quality, security, performance, and cost.
Validate that agent responses rely on approved authoritative data sources and map to defined domain controls, identifiers, measures, or calculation logic where applicable.
Implement refusal or human-escalation behavior when a request is outside approved scope, lacks authoritative evidence, or requires human judgment.
Partner with AI platform and technology teams to improve guardrails, usability, response clarity, and front-end experience without changing approved business logic.

4. Quality, Governance & Documentation

Implement data-quality and testing controls such as freshness checks, contract tests, anomaly detection, test automation, and clear alerting paths.
Ensure changes align with applicable architecture, security, privacy, platform, model-governance, and responsible-AI standards.
Maintain technical documentation, operating procedures, training materials, data lineage, governance records, and service-landscape knowledge.
Act as an effective first-line tester for changes and enhancements, with clear evidence of validation and traceability.
Influence architecture and tooling decisions, balancing delivery speed with maintainability, supportability, scalability, and compliance.

5. Business Partnership & Capability Building

Translate business needs, ambiguous requirements, production findings, and user feedback into prioritized, deliverable increments.
Consult business and technology stakeholders on proposed changes and communicate technical implications clearly to non-technical audiences.
Partner with product owners, risk or control specialists, data and analytics teams, and platform teams to deliver solutions that preserve approved business logic.
Train end users on effective prompt usage, solution boundaries, escalation paths, and good practices.
Mentor engineers and colleagues through pairing, code review, onboarding, troubleshooting support, and practical knowledge sharing; act as a go-to technical advisor within the team.

6. Development Capability (ability to fulfil at least 1 of the following specialisms)