Lead Agentic AI Engineer

ENGINEERING / ARTIFICIAL INTELLIGENCE / PLATFORM PRODUCTS

Lead Agentic AI Engineer

Platform Engineering | AWS & Azure | Multi-Agent Systems

EXPERIENCE
13+ years (15+ preferred)

ROLE TYPE
Hands-on technical lead

CLOUD
AWS / Azure platform

About the opportunity

Tachyon Technologies is expanding its enterprise AI engineering capabilities. We are seeking experienced, hands-on engineering leaders to design and build secure, scalable agentic AI solutions, reusable platform components, and cloud-native AI products. This is an opportunity to build production-ready systems rather than isolated proof-of-concept chatbots.

This is a hands-on product engineering and technical leadership position. You will own complex AI engineering features end to end, make architectural decisions with the broader engineering team, write and review production code, mentor engineers, and help deliver reliable agentic capabilities across cloud environments.

What you will own

• Agent engineering & runtime: Design and build AI agents, multi-agent workflows, orchestrators, tool-calling services, human-in-the-loop approvals, state and memory management, agent templates, and reusable deployment/runtime components.
• Frameworks & interoperability: Build real systems using Python and agent frameworks such as LangGraph, Microsoft Agent Framework, AutoGen/AG2, Google ADK or equivalents. Engineer MCP servers, tool gateways, and agent-to-agent (A2A) integration patterns.
• Multi-cloud platform engineering: Develop secure, scalable services on AWS and/or Azure; contribute to cloud-portable architecture, containerized deployment, infrastructure as code, CI/CD, performance, resilience, and runtime operations.
• Enterprise connectivity & context: Build secure connectors to APIs, ITSM, ERP and enterprise knowledge systems. Implement permission-aware RAG, hybrid retrieval, contextual filtering, session memory, and reliable workflows that act on business systems.
• Security, observability & quality: Design for identity and least privilege, tool policies, prompt-injection defenses, PII handling, audit trails, tracing, evaluation datasets, automated quality gates, observability, and cost/latency optimization.
• Product feature delivery: Translate product requirements into technical designs and working increments; own technical quality, design/code reviews, testing, troubleshooting, and feature readiness from prototype through production.
• Team leadership: Lead and coach engineers, raise coding standards, collaborate across architecture/product/delivery, resolve technical blockers, and remain personally involved in critical implementation work.

Required experience & skills

• Senior engineering foundation: 13+ years in software/platform engineering, with strong experience designing and shipping distributed applications, APIs, microservices, databases, asynchronous workflows, and enterprise integrations.
• Hands-on development: Expert Python development is essential. Strong backend engineering, debugging, unit/integration testing, code review, and system design skills are required; Java, C#, or TypeScript experience is valuable.
• Applied agentic AI: Proven hands-on development of GenAI/agentic applications beyond prompt-only demos; experience with agent orchestration, RAG, tool use, stateful workflows, evaluation, failure handling, and integration with real enterprise systems.

Cloud, production & leadership

• AWS / Azure: Deep implementation experience with at least one of AWS or Azure and demonstrable experience on the other. Ability to design portable services while using appropriate cloud-native managed capabilities.
• Production ownership: Experience deploying and supporting cloud-native products with authentication/authorization, logging, monitoring, CI/CD, performance engineering, scaling, secure networking, and cost control.
• Technical leadership: Evidence of leading engineering teams, independently owning complex features, making design trade-offs, mentoring developers, and collaborating with stakeholders on delivery decisions.

Cloud specialization (choose a primary track)

AWS platform track

Amazon Bedrock / Bedrock AgentCore, IAM, Lambda, ECS/EKS, API Gateway, S3, networking, CloudWatch and production agent deployment patterns.

Azure platform track

Microsoft Foundry / Agent Service, Microsoft Agent Framework, Azure OpenAI, Azure AI Search, Entra ID, AKS, Azure Functions, Key Vault, Application Insights and managed identities.

Strong advantages

• Platform product background: Experience building SDKs, developer tools, agent builders, template frameworks, internal platforms, marketplaces, workflow engines, or multi-tenant SaaS components.
• Data & advanced AI: Knowledge of Databricks/Snowflake, Neo4j/knowledge graphs, multimodal retrieval, model routing, semantic caching, fine-tuning, model evaluation, or smaller open-source models.
• Enterprise ecosystems: Experience integrating SAP, Salesforce, ServiceNow, Jira, Microsoft 365, Confluence, GitHub or similar enterprise platforms; familiarity with GCP and private-cloud deployment is a plus.

What success looks like

• Ships well-designed, tested, observable agentic AI capabilities that can be reused across enterprise use cases and cloud environments.
• Improves reliability, security, traceability, evaluation quality, developer experience, and cost/latency of production agent workflows.
• Builds a stronger engineering team while continuing to contribute directly to difficult technical problems and product innovations.

Who will thrive in this role

A builder who enjoys owning product features from whiteboard to production, can explain and defend engineering trade-offs, and is equally comfortable designing a robust multi-agent architecture and debugging an unreliable tool call. The role is not intended for purely managerial, governance-only, or low-code/prompt-only profiles.

Tachyon Technologies is an equal opportunity employer.