Director of Engineering (Agentic AI)

We are seeking an experienced and visionary Director of Engineering - Agentic AI to lead the architecture, development, and scaling of next-generation AI systems powered by Large Language Models (LLMs), autonomous agents, and multi-agent orchestration frameworks. In this role, you will lead multiple engineering teams building intelligent agent platforms capable of reasoning, planning, memory management, tool use, and autonomous decision-making. You will work closely with Product, Research, Data Science, and Infrastructure teams to deliver production-grade AI applications that transform business operations and customer experiences. This is a strategic leadership role requiring deep technical expertise, strong people management skills, and a passion for driving innovation in the rapidly evolving field of Agentic AI.

The core responsibilities for the job include the following:

Engineering Leadership:

• Lead and mentor engineering managers, tech leads, and software engineers across AI platform and application teams.
• Define engineering strategy, roadmap, and execution plans for Agentic AI initiatives.
• Establish best practices for coding, architecture, testing, deployment, and monitoring.
• Drive hiring, performance management, and career development.

Agentic AI Architecture:

• Design and oversee the development of autonomous AI agent systems with: Planning and reasoning, Long- and short-term memory, Tool/function calling, Multi-agent collaboration, Reflection and self-correction.
• Build scalable orchestration frameworks using technologies such as LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, and Semantic Kernel.

Generative AI and LLM Systems:

• Develop applications leveraging leading foundation models from OpenAI, Anthropic, Google DeepMind, Meta, and Mistral AI.
• Implement Retrieval-Augmented Generation (RAG), embeddings, vector search, and fine-tuning strategies.
• Build guardrails for hallucination reduction, prompt security, and policy enforcement.

Platform and Infrastructure:

• Architect highly scalable cloud-native systems on Amazon Web Services, Google Cloud, and Microsoft Azure.
• Utilize Docker, Kubernetes, Terraform, and Apache Kafka.
• Integrate vector databases such as Pinecone, Weaviate, Milvus, and Qdrant.

AI Governance and Observability:

• Establish frameworks for evaluation, benchmarking, and continuous improvement.
• Implement observability tools such as LangSmith, Weights and Biases, Arize AI, and Helicone.
• Ensure compliance with security, privacy, and responsible AI standards.

Cross-Functional Collaboration:

• Partner with Product and Research to identify high-value AI use cases.
• Collaborate with Legal, Security, and Compliance teams.
• Communicate architecture decisions and executive updates to leadership and stakeholders.

Requirements:

• Bachelor's or Master's degree in Computer Science, Engineering, AI, or a related field.
• 12-18+ years of software engineering experience.
• 5+ years in senior engineering leadership roles (Director, Senior Manager, or Head of Engineering).
• Hands-on experience building and deploying LLM-based applications and AI agents.
• Strong programming experience in Python and/or Java.
• Deep understanding of: Prompt engineering, RAG architectures, Embeddings and vector search, Model evaluation, and Multi-agent systems.
• Expertise in distributed systems and cloud-native architecture.
• Experience managing multiple engineering teams and delivering large-scale systems.

Preferred Qualifications:

• Experience with reinforcement learning, fine-tuning, or model distillation.
• Background in MLOps and AI platform engineering.
• Familiarity with AI safety, governance, and regulatory frameworks.
• Contributions to open-source AI projects.
• Experience in enterprise SaaS, fintech, healthcare, or other regulated domains.

Key Skills:

• Technical Skills: Agentic AI Architecture, Multi-Agent Systems, Large Language Models (LLMs), RAG and Vector Databases, and Prompt Engineering. Python, Java, Cloud Platforms (AWS/GCP/Azure), Kubernetes, Docker, Terraform, AI Observability and Evaluation, and Distributed Systems.

Leadership Skills:

• Strategic Planning.
• Team Building and Mentoring.
• Technical Program Execution.
• Stakeholder Management.
• Innovation Leadership.