Lead Engineer - Ad Tech

As a Lead Engineer (Ad Tech & Applied AI) , you will provide technical leadership across backend platforms and emerging AI-powered capabilities.

You will collaborate with cross-functional teams to help define the technology strategy for Ad Tech platforms, including DSP, SSP and Ad Servers, supporting self-service advertising needs. You will assess build-versus-buy decisions for new capabilities through POCs and prototypes while considering long-term architecture, scalability, reliability and operational trade-offs.

A key part of this role will be designing and buildin g production-grade applications powered by Large Language Models and agentic systems from experimentation through production operation and continuous evaluation . We are looking for engineers who have moved beyond conversational AI prototypes and have experience engineering LLM-powered systems that operate reliably within real business workflows.

You will design architectures that combine deterministic software components with probabilistic AI capabilities, making deliberate decisions about where traditional code, rules and workflow engines should be used versus where LLM-driven reasoning and autonomous agents provide value.

You will lead engineering efforts to meet functional and non-functional requirements and assist teams in solving complex business challenges through scalable technical solutions.

You will work closely with engineering managers to build high-performing engineering teams and provide technical leadership, architecture guidance, coaching and mentoring. You will also participate in the selection of technical talent and contribute actively to Target’s broader technical community.

You have 8+ years of software development experience , with experience designing, building and operating complex distributed systems through at least one complete implementation lifecycle.

You have strong backend engineering fundamentals and are comfortable designing scalable APIs, microservices, asynchronous systems and data-intensive applications.

You are fluent in Java / Spring and microservices architecture , with experience building highly available production systems.

You have experience working with databases including RDBMS and NoSQL technologies such as Cassandra and MongoDB , and understand data modeling and storage trade-offs.

You have experience building distributed event-driven architectures using technologies such as Kafka .

You understand Ad Tech business fundamentals and how technology supports business objectives, and can translate business vision into technical strategy while understanding architectural and financial trade-offs.

Experience building or integrating DSP, SSP or Ad Server technology platforms in support of self-service advertising is preferred.

You have hands-on experience designing, building and operating production applications using LLMs , beyond chat interfaces, prompt experimentation and proof-of-concept applications.

You have designed and optimized production-grade RAG systems , with strong understanding of data ingestion and chunking, retrieval and ranking, context and grounding, hybrid retrieval, and evaluation .

You have experience with knowledge graphs and graph-based retrieval , and understand when to use vector search, Graph RAG, structured queries, traditional search or hybrid approaches based on the problem and data.

You understand the engineering trade-offs of production LLM systems, including quality, latency, cost, reliability, observability, security and failure handling , and can systematically improve retrieval and overall system performance rather than relying primarily on prompt engineering.

You have designed or built agentic applications or orchestration frameworks where LLM-powered components interact with tools, APIs, retrieval systems and other agents to accomplish multi-step tasks.

You understand concepts such as:

Tool/function calling

Agent planning and execution

Workflow and state management

Multi-agent orchestration

Agent memory and context management

Routing and delegation

Human-in-the-loop workflows

Guardrails and policy enforcement

Retry, timeout and fallback strategies

Agent observability and traceability

Evaluation of agent behavior and task completion

You understand the distinction between deterministic and non-deterministic execution paths and can design systems that deliberately combine both.

You know when a business workflow should remain deterministic and testable using conventional software and when probabilistic reasoning using an LLM or agent is appropriate .

You design AI systems assuming that model outputs can be incorrect or unpredictable and therefore incorporate appropriate validation , constraints, structured outputs, fallbacks, idempotency, observability and human intervention where required .

You understand that production AI systems require rigorous quality and evaluation practices beyond model selection and prompting.

You have experience establishing offline and online evaluation strategies for LLM-powered systems, covering retrieval and response quality, factual grounding , task completion, reliability, latency and cost.

You can build evaluation harnesses and regression tests with representative datasets, metrics and quality thresholds, and integrate them into CI/CD as deployment and quality gates .

You can use evaluation, observability and tracing to diagnose quality issues across data, retrieval, context, prompts, models and orchestration , and drive systematic improvements.

You can translate ambiguous business problems into clear technical architectures and incrementally deliver solutions from experimentation through production.

You make architecture decisions based on measurable trade-offs rather than technology trends and are comfortable challenging unnecessary complexity.

You can lead POCs and technical experiments while clearly distinguishing between what demonstrates feasibility and what is required to operate the capability reliably at enterprise scale.

You have proven technical leadership capabilities and the ability to influence engineers, product leaders and cross-functional stakeholders.

You enjoy mentoring engineers and raising the technical capabilities of the broader engineering team.

You collaborate effectively with Product and domain experts and can communicate complex architecture decisions to both technical and non-technical stakeholders.

You stay current with evolving engineering and AI technologies through formal training and self-directed learning, while applying new technologies pragmatically.

You have experience working within CI/CD and DevOps environments and understand production engineering practices including monitoring, observability, resilience, security and operational readiness.

Life at Target: https://india.target.com/

Benefits: https://india.target.com/life-at-target/workplace/benefits

Culture: https://india.target.com/life-at-target/belonging