Specialist - Tech Development

Overview

Grade 8 individual contributor supporting the design, build, deployment and operation of reliable data pipelines and AI-enabled solutions, with accountability for production reliability, observability, trusted data access, prompt effectiveness and responsible operation in a regulated environment.

What you'll do

• 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.
• Control and monitor approved data access points across structured and unstructured sources, cloud platforms, documentation and authoritative business repositories.
• 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, 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, and 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.
• Monitor system health, trace logs, response quality, hallucination indicators, data drift, failures and operational trends.
• Define and maintain 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.
• 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.
• Implement data-quality and testing controls such as freshness checks, contract tests, anomaly detection, test automation and clear alerting paths.
• Ensure changes align with 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 first-line tester for changes and enhancements, with clear evidence of validation and traceability.
• 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 to non-technical audiences.
• 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 knowledge sharing.
• Fulfil at least one development specialism: front-end development, data engineering, data science, AI engineering or full-stack development (as detailed in the source), and demonstrate at least two other listed specialisms.

What you'll need

• Bachelor's degree in computer science, engineering, data science, data engineering or a related field, or equivalent practical experience.
• Typically 4 years of relevant hands-on experience building, operating and improving production data, software, AI or cloud solutions.
• Strong Python and SQL skills, with experience developing maintainable production code, APIs, tests and automation.
• Hands-on experience with Microsoft Azure services and concepts such as Databricks, Azure Data Factory, Web Apps or App Services, Key Vault, storage and deployment practices.
• Experience with modern data engineering technologies, including data modeling, Spark, orchestration or transformation tooling, and warehouse or lakehouse patterns.
• Experience with AI or application frameworks such as LangChain or related LLM frameworks, FastAPI, Pydantic and API gateway patterns such as Kong.
• Working knowledge of Docker and Kubernetes, plus source control and engineering artefacts including GitHub, JSON, JavaScript, Jupyter, YAML and shell scripting.
• Practical experience using AI coding assistants as part of daily engineering work, with disciplined review of generated output.
• Strong communication, stakeholder-management and mentoring skills for influencing business, product, governance, architecture, security, data and engineering teams.

Nice to have

• Master's degree or higher in software engineering, computer science, data science or a related field.
• Experience creating or significantly extending AI agents in an Azure environment using Python, web applications and enterprise data sources.
• Knowledge of large language models, generative AI, prompt engineering, retrieval-augmented generation, embeddings, vector stores and fine-tuning techniques.
• Experience with unstructured data, data contracts, data-quality frameworks, catalogs, lineage tools and observability platforms.
• Familiarity with model governance, privacy-preserving techniques, responsible AI, bias mitigation and controls for high-stakes or regulated solutions.
• Experience in financial risk management, finance controlling, controls testing or another regulated business domain.
• Experience working effectively in international, distributed and cross-functional environments.

Details

• Location: Bengaluru Luxor North Tower.