AI Developer- Agentic Systems and LLM Engineering
In this role, we are seeking a highly skilled AI Developer to design and build next-generation agentic AI systems that can reason, plan, and act autonomously. This role sits at the intersection of LLM engineering, system design, and applied AI, with a strong focus on building scalable, production-grade intelligent agents.
You will work closely with prompt engineers, data scientists, and platform teams to develop AI components that leverage retrieval-augmented generation (RAG), memory systems, and tool integration frameworks.
Responsibilities:
Agentic AI Development
- Design and build AI agents capable of reasoning, planning, and executing tasks autonomously
- Develop modular agent components (planners, executors, evaluators, orchestrators)
- Implement multi-step workflows combining LLMs with external tools and APIs
Framework & System Design
- Build and extend agent frameworks for orchestration, chaining, and decision-making
- Develop memory systems (short-term and long-term) to improve agent context retention
- Implement tool-use capabilities, enabling agents to interact with databases, APIs, and enterprise systems
RAG & Knowledge Systems
- Design and implement Retrieval-Augmented Generation (RAG) pipelines
- Work with embeddings and vector databases to enable semantic search and contextual reasoning
- Optimize document ingestion, chunking, indexing, and retrieval strategies
Collaboration & Integration
- Collaborate with prompt engineers to refine prompts, evaluation strategies, and agent behaviors
- Work with cloud and platform teams to deploy scalable AI services
- Integrate AI solutions into enterprise workflows and applications
Minimum Qualifications
Technical Skills
- Strong programming expertise in Python
- Hands-on experience with LLMs and agentic frameworks (e.g., LangChain, Semantic Kernel, or similar)
- Solid understanding of:
- Embeddings and vector databases (e.g., Pinecone, FAISS, Azure AI Search)
- RAG architectures and pipelines
- Prompt engineering and evaluation techniques
- Cloud & Infrastructure
- Good understanding of the Azure cloud ecosystem, including:
- Azure OpenAI Service
- Azure Functions / App Services
- Azure AI Search or equivalent services
- Experience deploying scalable AI/ML solutions in cloud environments
- System Thinking
- Ability to design modular, scalable, and reusable AI components
- Understanding of state management, memory architectures, and agent orchestration
- Strong problem-solving and analytical thinking
- Ability to work in a cross-functional, fast-paced environment
- Clear communication skills to collaborate with both technical and non-technical stakeholders
Qualifications/ Skills
- Experience building multi-agent systems
- Familiarity with evaluation frameworks for LLMs (hallucination detection, response quality, etc.)
- Exposure to MLOps / LLMOps practices
- Knowledge of API integrations and microservices architecture
- Experience in enterprise AI deployments (banking, risk, or financial services preferred)
- Contributions to open-source AI/LLM projects
- Familiarity with agent evaluation benchmarks and guardrails
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