Senior Agentic AI Engineer

ENGINEERING / ARTIFICIAL INTELLIGENCE / CLOUD PRODUCTS

Senior Agentic AI Engineer

Hands-on AI Development | AWS & Azure | Enterprise Agents

EXPERIENCE
9-12 Years

ROLE TYPE
Hands-on senior engineer

CLOUD
AWS / Azure

About the opportunity

Tachyon Technologies is seeking a strong, hands-on Senior Agentic AI Engineer to build secure, scalable AI agents, intelligent workflows, and cloud-native GenAI applications. You will turn product ideas and technical designs into reliable, reusable features used in real enterprise environments.

This is an individual-contributor engineering role with meaningful feature ownership. You will write production code, design components, integrate enterprise systems, troubleshoot agent behavior, and collaborate with engineering leads and architects to take solutions from prototype to production.

What you will build

• Agent development & orchestration: Implement AI agents, tool calling, multi-step and multi-agent workflows, human approvals, state management, memory, retries, and safe error recovery using Python.
• Frameworks & protocols: Develop with frameworks such as LangGraph, Microsoft Agent Framework, AutoGen/AG2, Google ADK, or similar. Build and consume MCP tools; integrate agent-to-agent (A2A) patterns where appropriate.
• Cloud-native AI services: Build and deploy agent services on AWS or Azure using managed AI models, API services, functions, containers, messaging, storage, secrets, and monitoring.
• RAG & enterprise integrations: Build document ingestion and retrieval pipelines, metadata filters, hybrid/vector search, grounded responses, and integrations with enterprise APIs, knowledge bases, and operational systems.
• Testing, safety & observability: Add structured logging and traces, agent evaluations, regression tests, tool-call validation, guardrails, identity-aware access, prompt-injection defenses, and data protection.
• Feature delivery: Own assigned features from design and implementation through unit/integration testing, CI/CD, deployment, documentation, and production defect resolution.
• Engineering collaboration: Work with product owners, architects, cloud engineers, QA, and other developers; participate in design reviews, code reviews, and continuous improvement.

Required experience & skills

• Engineering experience: 9+ years of professional software engineering experience (9-12 years preferred), including substantial backend/cloud development and experience delivering production systems.
• Python & backend: Strong hands-on Python expertise, APIs (FastAPI or equivalent), async workflows, microservices, SQL/NoSQL, Git, debugging, testing, and clean software design.
• Applied GenAI & agents: Hands-on experience building and integrating LLM-powered applications using RAG, model APIs, agent orchestration, tool/function calling, structured output, and error handling.

Cloud engineering & production readiness

• AWS / Azure: Strong implementation experience on at least one platform (AWS or Azure), and readiness to work across both. Understand cloud identity, networking, containers/functions, APIs, monitoring, and secure deployment.
• Production delivery: Experience with CI/CD, Docker, configuration and secrets, authentication and authorization, instrumentation, performance testing, and incident troubleshooting.
• Independent ownership: Able to break a feature into implementation tasks, estimate effort, explain trade-offs, write maintainable code, review peers’ work, and communicate blockers early.

Cloud specialization (primary platform)

AWS track

Amazon Bedrock / Bedrock AgentCore, IAM, Lambda, ECS/EKS, API Gateway, S3, CloudWatch, VPC, and secure model/tool integrations.

Azure track

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

Strong advantages

• Reusable platform engineering: Experience developing SDKs, shared libraries, workflow components, agent templates, connector frameworks, or multi-tenant SaaS services.
• Advanced AI & data: Exposure to evaluation frameworks, OpenTelemetry, model routing/caching, semantic search, knowledge graphs, multimodal AI, open-source models, or ML serving.
• Enterprise ecosystem: Integrations with SAP, Salesforce, ServiceNow, Jira, Microsoft 365, Confluence, GitHub, or data platforms such as Snowflake and Databricks.

What success looks like

• Delivers working agentic AI features with clean code, documented interfaces, automated tests, and reliable production behavior.
• Improves agent accuracy, tool-call reliability, security, observability, and cost/latency based on real evaluation results.
• Independently resolves technical problems, contributes reusable components, and raises engineering quality through collaboration and code review.

Who will thrive in this role

An engineer who enjoys writing code and debugging complex AI workflows, understands the difference between an impressive demo and a production system, and can independently deliver meaningful features. This is not a prompt-only, low-code-only, or project-coordination role.

Tachyon Technologies is an equal opportunity employer.