Engineering Lead-AI Agentic Platform

Engineering Lead

AI Agentic Platform Orchestration, Benchmarking & Unification

About The Role We are seeking an experienced Engineering Manager to build, architect, and lead the team behind our next-generation AI Agentic Platform. This platform empowers enterprises to measure ROI on industry-specific agent activity, benchmark AI agents against performance standards, orchestrate complex multi-agent workflows, and unify AI agents seamlessly across diverse platforms and ecosystems.

As the engineering leader for this strategic product, you will combine deep technical expertise with strong people management skills to deliver a unified control plane for the rapidly evolving agentic AI landscape. You will own the technical vision, drive architectural decisions, and grow a high-performing team that ships at the frontier of AI engineering.

Platform Capabilities You’ll Own

• Industry-Specific ROI Measurement: Build instrumentation, analytics, and reporting frameworks that quantify the business impact of AI agents across verticals such as BFSI, healthcare, retail, and telecom.
• AI Agent Benchmarking: Develop standardized evaluation harnesses, performance scoring, and quality metrics that allow enterprises to compare agents on accuracy, latency, cost, safety, and task success.
• Agent Orchestration: Design multi-agent orchestration engines, workflow planners, routing layers, and coordination frameworks that enable agents to collaborate on complex tasks reliably.
• Cross-Platform Agent Unification: Architect a unified abstraction layer, connectors, and protocol adapters (MCP, A2A, OpenAI Assistants, LangGraph, etc.) so customers can manage agents across vendors and frameworks from a single pane of glass.

Key Responsibilities

• Hire, build, and lead a high-caliber engineering team across backend, AI/ML, and platform infrastructure
• Define and own the technical architecture for a multi-tenant, scalable AI agentic platform
• Drive the technical roadmap for orchestration, benchmarking, observability, and unification capabilities
• Partner with Product, Design, Data Science, and GTM teams to translate customer needs into shippable engineering outcomes
• Establish engineering best practices for AI agent development, evaluation, safety, and deployment
• Lead architectural reviews, design discussions, and technical decision-making for complex distributed systems
• Build robust evaluation frameworks and ROI dashboards that customers and internal teams trust
• Ensure platform reliability, performance, security, and cost efficiency at scale
• Mentor engineers, set growth paths, run performance reviews, and foster an inclusive, high-trust team culture
• Communicate progress, risks, and strategic recommendations to executive leadership

Required Qualifications

• 8+ years of software engineering experience with at least 3+ years managing engineering teams
• Proven experience architecting and scaling distributed systems, microservices, and cloud-native platforms
• Hands-on experience building AI/ML or LLM-powered products in production environments
• Strong understanding of agentic AI concepts including planning, tool use, memory, and multi-agent coordination
• Proficiency in modern programming languages such as Python, Go, Java, or TypeScript
• Experience designing APIs, SDKs, and integration platforms used by enterprise customers
• Track record of recruiting, developing, and retaining strong engineering talent
• Strong communication, stakeholder management, and cross-functional collaboration skills
• Experience with cloud platforms (AWS, GCP, or Azure), containerization (Docker, Kubernetes), and CI/CD pipelines
• Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience

Preferred Qualifications

• Experience building agent orchestration frameworks (LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar)
• Familiarity with emerging agent interoperability standards such as Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocols
• Experience designing LLM evaluation and benchmarking systems (offline evals, A/B testing, golden datasets, judge models)
• Knowledge of observability for AI systems including tracing, telemetry, and cost/quality monitoring
• Experience with retrieval-augmented generation (RAG), vector databases, and prompt engineering at scale
• Background in building analytics, ROI dashboards, or business intelligence tooling for enterprise…