Engineering Lead (AI & Automation Products)

Merkle Italia; Karnataka; On-site; INR 600,000 - 1,100,000; Full time

Job summary

Merkle India is seeking an experienced senior software engineer with 12–16 years of Python expertise to lead engineering teams and architect production-grade APIs using Python frameworks such as FastAPI or Flask. The role also involves building full-stack applications with React and Tailwind CSS on the frontend, and collaborating across onshore and offshore teams.

Candidates should be adept at designing scalable database schemas (Postgres preferred) and integrating AI-assisted development tools

Qualifications

• 12–16 years of professional software engineering experience with deep Python expertise.
• Demonstrated experience leading or managing a team of engineers.
• Strong experience designing and building production APIs in Python (FastAPI, Flask, or similar) and full-stack applications including React + Tailwind CSS front ends.

Job description

Job Description

Location: DGS – India (overlap hours with US Eastern Time required)

Required Qualifications

• 12–16 years of professional software engineering experience with deep Python expertise
• Demonstrated experience leading or managing a team of engineers — code review, mentoring, growth planning — not just individual contribution
• Strong experience designing and building production APIs in Python (FastAPI, Flask, or similar) and full-stack applications including React + Tailwind CSS front ends
• Strong relational database experience — schema design, normalization, query performance — Postgres preferred
• 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 integrating LLM APIs (Claude, OpenAI, or equivalent) into production systems — system prompt design, structured output parsing, multimodal input handling
• 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
• Strong context engineering fundamentals — context window management, token budgeting, long-document handling strategies, and retrieval/context-selection patterns
• 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 integrating third-party platform APIs with OAuth (any domain) — general competency, not platform-specific
• Working knowledge of secrets management and credential security practices in production systems, ideally including Azure Key Vault or equivalent managed secrets tooling
• 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
• Practical understanding of human-in-the-loop AI systems — adjudication workflows, labeled examples, accuracy measurement by parameter/category, feedback loops, and quality gates
• Experience with cloud infrastructure (Azure preferred) and modern deployment patterns: containers, CI/CD, managed identities, object storage, and background job/workflow execution
• Experience implementing background-processing or workflow patterns — queues, scheduled jobs, retries, idempotency, status tracking, and operational monitoring
• Strong written and verbal communication for collaboration across distributed onshore (US) and offshore (India) teams

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
• Exposure to model selection and cost optimization strategies — prompt caching, batching, tiered model selection by task complexity, latency/cost tradeoff analysis, and usage forecasting
• Background in media, advertising, or marketing technology data environments
• Exposure to data governance tooling such as Unity Catalog, attribute-based access control, or tag-driven policies
• 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
• 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
• Exposure to DV360 SDF (Structured Data Files), TTD API, or comparable adtech platform data formats/APIs
• Open-source contributions or public projects demonstrating full-stack or AI engineering work

Location: DGS India - Bengaluru - Manyata N1 Block

Brand: Merkle

Time Type: Full time

Contract Type: Permanent

About dentsu

For over 120 years, innovation has been a core tenet of our offering – exploring new ways to reach, engage and nurture relationships with audiences. Together we drive a multiplier effect for clients at a global scale, through the development of Integrated Growth Solutions that are underpinned by our promise to clients: innovating to impact. Be a force for good. Sustainability is a vital part of our business and an important area of focus for our clients. We’re leading the way – helping to build a more sustainable planet. Dream loud. In this moment of transformation, we need our people to be fearless, embracing change and ambiguity, driven by the love for their work and excitement for the future. Team without limits. We create opportunities for connection and collaboration between our colleagues and clients, building a sense of belonging and having some fun along the way. Find out more about us

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