Associate Director - Generative AI Engineer

## Role Summary

We are hiring a senior individual contributor to design, build, and productionize end-to-end agentic AI systems for high-impact enterprise workflows. This role is for a hands-on builder who can own architecture, coding, evaluation, reliability, and deployment at scale.

You will work on complex AI programming problems that require deep engineering judgment across multi-agent orchestration, MCP integrations, secure tool usage, retrieval systems, and cloud deployment.

## Why This Role Is Different

• You will own the full lifecycle: problem framing, architecture, implementation, deployment, observability, and continuous optimization.

• You will work on hard, real-world AI systems where accuracy, latency, reliability, and governance matter together.

• You will influence engineering quality standards across agent design, LLMOps, and production guardrails while remaining a strong individual contributor.

## Core Responsibilities

• Build end-to-end agentic AI systems from scratch using robust software engineering and AI systems design principles.

• Design and implement multi-agent workflows (planner, executor, reviewer, verifier, fallback) with clear control flows and reliability mechanisms.

• Build and integrate MCP-compatible tool ecosystems, including secure access controls, tool permissioning, and auditable execution patterns.

• Architect and optimize RAG pipelines with vector databases, structured retrieval, reranking, and context management.

• Define and enforce output quality standards using schema-driven generation, validation layers, and guardrails.

• Establish evaluation pipelines for correctness, groundedness, robustness, cost, and latency.

• Lead cloud-native deployment and runtime optimization on AWS or Azure, including scalable inference and operational monitoring.

• Drive incident triage and production debugging for agentic workflows.

• Translate business goals into AI system designs with measurable outcomes.

• Mentor junior engineers through design reviews, code reviews, and problem decomposition.

## Must-Have Technical Skills

• 9 to 12 years of software engineering experience with advanced Python expertise.

• Strong command of software design patterns and architecture for complex distributed systems.

• Working depth in deep learning foundations: neural networks, transformers, embeddings, and modern LLM behavior.

• Proven experience building and shipping production agentic AI systems, not only prototypes.

• Proven experience with multi-agent orchestration frameworks and patterns.

• Proven experience implementing MCP server-client integrations in secure enterprise contexts.

• Strong RAG engineering skills with vector databases and retrieval quality tuning.

• Hands-on LLMOps capability: experiment tracking, evaluation frameworks, model monitoring, and release governance.

• Strong prompt, context, and harness engineering capability for high-fidelity outputs.

• Strong understanding of data security and governance controls for AI systems.

## Preferred Skills

• Proficiency in additional programming languages beyond Python.

• Experience with advanced inference optimization (for example multi-GPU sharding, quantization, hardware-specific runtimes).

• Experience with enterprise integration patterns across APIs, data platforms, and workflow systems.

• Experience with financial or document-intelligence style structured extraction systems.