Agentic AI Engineer
advanced
Job Description
- Agent Architecture & Development
- Design and implement agentic AI architectures, including:
- ReAct (Reason + Act)
- Plan‑and‑Execute
- Tool‑using agents
- Multi‑agent systems and coordinators
- Build LLM‑powered applications using modern foundation models
- Translate business use cases into reliable, scalable agent workflows with clear handoffs and fallbacks
- LLM & RAG Engineering
- Develop and optimize prompt engineering strategies for reasoning, task execution, and tool use.
- Implement Retrieval‑Augmented Generation (RAG) pipelines using structured and unstructured data.
- Design and manage agent memory systems, including:
- Short‑term (conversation/state)
- Long‑term (vector‑based memory)
- Work with vector databases
- Tooling, APIs & Integration
- Build and integrate internal and external tools (APIs, microservices, enterprise systems).
- Enable agents to safely interact with:
-
- Databases
- Business applications
- Automation platforms
- Cloud services
- Ensure robust error handling, retries, and fallback logic.
- Production Readiness & Operations
- Deploy agentic systems into production environments.
- Implement monitoring, logging, and evaluation frameworks for:
- Accuracy and task success
- Latency and cost efficiency
- Safety and policy compliance
- Design and enforce guardrails to prevent hallucinations, unsafe actions, or data leakage.
- Continuously optimize system performance and cost
- Collaboration & Delivery
- Partner closely with business stakeholders, application developers, data teams, and business stakeholders.
- Act as a technical product owner for agentic solutions: collect user and business requirements, clarify problem statements, and translate them into system designs and implementation plans
- Identify high‑impact AI opportunities and rapidly prototype, validate, and scale solutions.
- Contribute to internal best practices, architecture standards, and AI governance.
- Lead agentic tooling decisions: evaluate frameworks, model providers, orchestration patterns, and integration approaches; make pragmatic build/buy choices aligned to security, reliability, and cost constraints.
- Act as a technical product partner: collect user requirements, define success metrics and acceptance criteria, translate needs into solution designs and implementation plans, and drive delivery from prototype to production.
- Required Qualifications
- Bachelor’s degree in Computer Science, Computer Engineering, Information Technology, or equivalent experience.
- 3+ years of professional software development experience.
- Strong proficiency in Python (required).
- Hands‑on experience working with LLM APIs, and MCP
- Solid understanding of:
-
- Prompt engineering
- Embeddings and semantic search
- Retrieval‑Augmented Generation (RAG)
- API and backend system design
- Experience integrating AI systems into production applications.
- Familiarity with cloud platforms (Azure, AWS, or GCP).
- Strong problem‑solving skills and ability to manage multiple priorities in fast‑paced environments.
- Excellent written and verbal communication skills in English
- Preferred Skills
- Experience with agent frameworks (e.g., LangChain, Semantic Kernel, AutoGen, CrewAI).
- Knowledge of multi‑agent coordination patterns.
- Experience with evaluation frameworks for LLMs and agents.
- Exposure to security, privacy, and compliance considerations in AI systems.
- Familiarity with CI/CD pipelines and DevOps practices.
- Experience building AI solutions for enterprise or internal business use cases
- What Success Looks Like
- Full‑time role: on‑site. <