Job Description

Locations

• DGS India - Bengaluru - Manyata N1 Block

Skills Required

• deep Python expertiseintermediate
• Claude Codeintermediate
• tool-use/function-calling patterns — defining tool schemasintermediate
• secrets managementintermediate
• cloud infrastructureintermediate
• mediaintermediate

Required Qualifications

• 12–16 years of professional software engineering experience with deep Python expertise (experience, 16 years)
• Demonstrated experience leading or managing a team of engineers — code review, mentoring, growth planning — not just individual contribution (experience)
• Strong experience designing and building production APIs in Python (FastAPI, Flask, or similar) and full-stack applications including React + Tailwind CSS front ends (experience)
• Strong relational database experience — schema design, normalization, query performance — Postgres preferred (experience)
• Strong practical proficiency with Claude Code or similar AI-assisted development tools, including agentic coding patterns and context management — and the ability to establish team standards for effective use (experience)
• Experience integrating LLM APIs (Claude, OpenAI, or equivalent) into production systems — system prompt design, structured output parsing, multimodal input handling (experience)
• Practical experience with tool-use/function-calling patterns — defining tool schemas, validating arguments, handling tool results, chaining tool calls, and managing basic failure/retry behavior (experience)
• Strong context engineering fundamentals — context window management, token budgeting, long-document handling strategies, and retrieval/context-selection patterns (experience)
• Awareness of prompt injection, adversarial inputs, and untrusted-document risks in AI systems; ability to design guardrails for external briefs, trafficking sheets, platform exports, and other model-readable inputs (experience)
• Experience integrating third-party platform APIs with OAuth (any domain) — general competency, not platform-specific (experience)
• Working knowledge of secrets management and credential security practices in production systems, ideally including Azure Key Vault or equivalent managed secrets tooling (experience)
• Solid grasp of QA practices, data quality engineering, and AI evaluation: unit and integration testing, data validation, golden datasets, regression evals, structured-output checks, and observability (experience)
• Practical understanding of human-in-the-loop AI systems — adjudication workflows, labeled examples, accuracy measurement by parameter/category, feedback loops, and quality gates (experience)
• Experience with cloud infrastructure (Azure preferred) and modern deployment patterns: containers, CI/CD, managed identities, object storage, and background job/workflow execution (experience)
• Experience implementing background-processing or workflow patterns — queues, scheduled jobs, retries, idempotency, status tracking, and operational monitoring (experience)
• Strong written and verbal communication for collaboration across distributed onshore (US) and offshore (India) teams (experience)

Preferred Qualifications

• Exposure to LLM application and workflow frameworks beyond raw API calls: LangChain, LangGraph, CrewAI, Temporal, Azure Durable Functions, Celery/RQ, or equivalent agent/workflow tooling — useful as the portfolio expands into durable, multi-step automation in later phases (experience)
• Exposure to model selection and cost optimization strategies — prompt caching, batching, tiered model selection by task complexity, latency/cost tradeoff analysis, and usage forecasting (experience)
• Background in media, advertising, or marketing technology data environments (experience)
• Exposure to data governance tooling such as Unity Catalog, attribute-based access control, or tag-driven policies (experience)
• Exposure to MCP servers or MCP-based developer workflows, with interest in when MCP is preferable to direct APIs for reusable tools, resources, prompts, and agent context (experience)
• Exposure to data flywheel concepts — labeled corpora, adjudication data models, feedback capture, quality dashboards, and mechanisms that improve future AI behavior and inform phase-gate decisions (experience)
• Exposure to DV360 SDF (Structured Data Files), TTD API, or comparable adtech platform data formats/APIs (experience)
• Open-source contributions or public projects demonstrating full-stack or AI engineering work (experience)

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