AI Engineer

About the Role

We are seeking an innovative and hands-on AI Engineer with experience in building LLM-powered applications using frameworks such as LangChain, LLamaIndex, and agentic frameworks such as LangGraph, CrewAI etc.

In this role, you will develop intelligent, autonomous agents capable of reasoning, decision-making, and multi-step task execution. You will integrate these agents into our digital platforms to solve complex problems across domains such as customer support, knowledge discovery, automation, and data analysis.

Key Responsibilities

• Design and implement agent-based architectures using frameworks like LangGraph, CrewAI, Agno, or similar to orchestrate LLM behavior in multi-step workflows.
• Develop prompt engineering strategies, memory management, and agent coordination logic to ensure reliability and performance of AI agents.
• Build composable AI workflows by chaining tools, APIs, and third-party services that agents can invoke securely and intelligently.
• Integrate LLM agents into enterprise products and digital interfaces (e.g., chatbots, data pipelines, workflow automation tools).
• Collaborate with product teams to define use cases and translate them into autonomous agent workflows with measurable outcomes.
• Implement observability for agent behavior (e.g., traceability, tool usage logs, fallback flows).
• Ensure responsible AI practices, including guardrails, rate limiting, sandboxing, and human-in-the-loop design for critical agent actions.
• Drive innovation by experimenting with new LLMs (e.g., GPT-4o, Claude, Gemini) and fine-tuning or RAG-based enhancement strategies.

Required Skills

• 3+ years of experience in AI engineering or ML development, with at least 1 year in LLM-based system design.
• Proficiency in Python, with strong experience in frameworks such as LangGraph, CrewAI, Agno, LangChain, or AutoGen.
• Deep understanding of prompt engineering, tool integration, and LLM orchestration patterns.
• Solid knowledge of RESTful APIs, vector databases (e.g., Pinecone, FAISS, Weaviate), and retrieval-augmented generation (RAG).
• Familiarity with hosting and serving LLMs via Azure OpenAI, open-source models (Mistral, LLaMA2), or self-hosted inference servers.
• Experience with Docker, Kubernetes (AKS), and CI/CD pipelines for deploying AI agents at scale.
• Strong collaboration skills with an ability to work cross-functionally in agile product environments.

Preferred Qualifications

• Bachelor's or Master's in Computer Science, AI, Machine Learning, or equivalent.
• Prior implementation of multi-agent AI systems (negotiation, task planning, reflection, delegation).
• Experience with LangGraph for memory and stateful workflows, or Agno for domain-driven agent design.
• Understanding of LLMOps, vector search optimization, and embedding strategies.
• Knowledge of agent governance, safety constraints, and explain ability in autonomous AI behavior.
• Certifications in Microsoft Azure AI, OpenAI ecosystem, or advanced prompt engineering.