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

1. Design and implement agent-based architectures using frameworks like LangGraph, CrewAI, Agno, or similar to orchestrate LLM behavior in multi-step workflows.  
2. 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.
3. Integrate LLM agents into enterprise products and digital interfaces (e.g., chatbots, data pipelines, workflow automation tools).
4. Collaborate with product teams to define use cases and translate them into autonomous agent workflows with measurable outcomes.
5. Implement observability for agent behavior (e.g., traceability, tool usage logs, fallback flows).
6. Ensure responsible AI practices, including guardrails, rate limiting, sandboxing, and human-in-the-loop design for critical agent actions.
7. Drive innovation by experimenting with new LLMs (e.g., GPT-4o, Claude, Gemini) and fine-tuning or RAG-based enhancement strategies. 

Required Skills & Qualifications

1. Bachelor's or Master's in Computer Science, AI, Machine Learning, or equivalent.
2. 3+ years of experience in AI engineering or ML development, with at least 1 year in LLM-based system design.
3. Proficiency in Python, with strong experience in frameworks such as LangGraph, CrewAI, Agno, LangChain, or AutoGen.
4. Deep understanding of prompt engineering, tool integration, and LLM orchestration patterns.
5. Solid knowledge of RESTful APIs, vector databases (e.g., Pinecone, FAISS, Weaviate), and retrieval-augmented generation (RAG).
6. Familiarity with hosting and serving LLMs via Azure OpenAI, open-source models (Mistral, LLaMA2), or self-hosted inference servers.
7. Experience with Docker, Kubernetes (AKS), and CI/CD pipelines for deploying AI agents at scale.
8. Strong collaboration skills with an ability to work cross-functionally in agile product environments. 

Preferred Qualifications

• 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 explainability in autonomous AI behavior.

• Certifications in Microsoft Azure AI, OpenAI ecosystem, or advanced prompt engineering.