01Overview
Role
We are hiring an AI Engineering Manager to lead a team of full-stack AI engineers delivering GenAI-powered agents and applications built on React and Python (FastAPI). You will own technical architecture and delivery, grow and manage the team, and partner with product and business stakeholders to set direction for automation across HR, Payroll, SAP, and Client Delivery. This is a hands-on leadership role you will still review designs and guide complex technical decisions, but your primary leverage is through your team.
Responsibilities
Lead, mentor, and grow a team of full-stack AI engineers; own hiring, onboarding, performance management, and career development
Own the technical architecture for agent platforms spanning React front-ends, Python/FastAPI services, and LLM orchestration layers
Drive delivery planning, prioritization, estimation, and execution across multiple workstreams
Set and uphold engineering standards for code quality, testing, security, observability, and deployment
Partner with Product Managers, SMEs, and business leaders to shape the roadmap and turn business processes into an agentic automation strategy
Make build-vs-buy and framework decisions (LangChain, LangGraph, CrewAI, etc.) and guide RAG, multi-agent, and integration patterns
Ensure scalable, secure, and reliable systems integrating with enterprise platforms (Workday, SAP, Salesforce)
Remove blockers, manage technical risk and debt, and report progress and outcomes to leadership
Stay hands-on enough to review architecture and code and to guide the hardest technical decisions
Designing, training, and deploying machine learning models and Large Language Models (LLMs) into production environments. Developing scalable, cloud-native microservices using tools like Docker and Kubernetes.
Building end-to-end AI applications integrated into web, mobile, or API-first platforms. Managing CI/CD pipelines and observability for AI systems using tools such as GitHub Actions and MLflow.
Implementing vector databases, Retrieval-Augmented Generation (RAG) pipelines, and orchestration tools like LangChain and LlamaIndex. Building and integrating multi-agent frameworks (e.g., LangGraph, LangFlow) to enable autonomous AI task execution.
Translating complex business problems into software-engineered AI solutions aligned with best practices. Deploying on cloud platforms (AWS, GCP, Azure) and leveraging services such as SageMaker, Vertex AI, or Databricks.
Proficiency in Python and ML frameworks like PyTorch, TensorFlow, and Hugging Face Transformers. Collaborating in cross-functional engineering environments including front-end (React, Next.js) and platform teams.
Familiarity with LLM providers (OpenAI, Anthropic, Meta, etc.) and working with chat/image-based model outputs. Hands-on experience with agent-based frameworks for intelligent orchestration.
Additional experience or qualifications considered a plus:
Delivering AI applications using RAG, vector databases, and agent-based frameworks. Working with multimodal inputs/outputs (text, image, audio).
Fine-tuning models on domain-specific data. Applying Responsible AI practices including governance, bias mitigation, and ethics.
Ensuring consistency and quality in model outputs in production or regulated environments.
Contributing to open-source projects or AI/ML publications.
Full-stack development experience across front-end, back-end, and database layers. .
