AI Engineer – Agentic Systems (4–7 Years Experience)

About the Role

We are looking for an experienced AI Engineer who specializes in building agents and agentic systemsfrom task-orchestration agents to workflow automation agents retrieval-augmented agents research/coding agents multimodal agents and domain-specific autonomous agents.

This is a full-stack AI engineering role ideal for someone who loves shipping: rapid MVPs stable production high ownership and fast problem-solving. Candidates must have built and deployed at least two AI agents in production in the past 12 months and be comfortable operating in high-velocity environments.

Applications are only considered if submitted through our official platform

What Youll Do1. Build & Deploy AI Agents

Design build and ship agentic workflows across multiple domains (research agents coding assistants conversational agents (voice texts etc) reasoning agents scheduling agents analytics agents workflow automation bots etc.).

Own the end-to-end lifecycle: data ingestion reasoning action taking evaluation monitoring.

Build multi-step agents capable of autonomous planning context tracking memory tool use and API orchestration.

2. Agent Architecture & Infrastructure

Architect systems using modern agent stacks (LangChain LlamaIndex OpenAI Assistants Model Context Protocol (MCP) custom orchestration).

Build robust retrieval pipelines (RAG) vector embeddings caching layers and knowledge-grounding systems.

Integrate agents with external tools and systems (APIs SaaS apps CRMs internal services databases messaging platforms).

3. Productionization

Deploy agents as microservices with proper observability evals guardrails fallbacks and monitoring.

Optimize inference cost latency accuracy and task-completion rates.

Run systematic evaluations: function calling accuracy groundedness hallucinations long-context stability.

4. Collaboration & Product Work

Work closely with product managers domain experts and engineers to translate business workflows into agent behaviors.

Create reusable frameworks and libraries to accelerate subsequent agent builds.

Document and evangelize agent best practices internally.

Applications are only considered if submitted through our official platform

What You Bring

Required 47 years of hands-on experience in AI/ML engineering.

Successful deployment of at least two production AI agents in the past 12 months (not prototypes).

Expertise in:

LLMs: OpenAI Anthropic Gemini Llama DeepSeek

Agent frameworks: LangChain OpenAI Assistants custom orchestration state machines

Retrieval (RAG) vector DBs (Pinecone Weaviate Chroma PGVector)

API integration & tool-use architectures

Python/Node for server-side agent logic

Microservice deployments (Docker Kubernetes CI/CD)

Strong debugging skills across distributed systems prompt engineering inference optimization and agent reasoning traces.

Comfortable building MVPs in days and scaling them to stable production within weeks/months.

Nice to Have

Experience building MCP servers or integrating with MCP tools.

Experience with structured function-calling workflows (JSON schema tool plans agent graphs).

Background in building internal agent frameworks or automation engines.

Experience designing evaluation frameworks for agents (task completion metrics scenario tests).

Familiarity with workflow engines (Temporal Airflow Prefect).

Success Looks Like

In your first 36 months you will:

Build and deploy multiple agents that solve real business workflows.

Improve accuracy response quality and reliability of existing agents.

Establish a reusable internal agent framework to increase build velocity.

Contribute significantly to cost latency and performance improvements.

Become a core owner of agentic architecture and experimentation.

Please Note: All official communications will be sent from our parent company Vikara () and candidates are required to create an account on the Aithors platform (powered by Vikara) as part of our hiring process.