Agentic AI Engineer

Agentic AI Engineer
Experience: 4–8 Years
Location: Remote
Employment Type: Contract
Job Summary
We are looking for an experienced Agentic AI Engineer to design, develop, and deploy intelligent AI agents capable of reasoning, planning, using tools, and autonomously executing complex business workflows.
The ideal candidate will have strong hands-on experience with Generative AI, LLMs, Agentic AI, Python, AI/ML frameworks, RAG, vector databases, and cloud platforms. You will work closely with product, engineering, and business teams to build scalable, reliable, and production-ready AI agent solutions.
Key Responsibilities
Design and develop AI agents and multi-agent systems capable of reasoning, planning, decision-making, and autonomous task execution.
Build agentic workflows using frameworks such as LangGraph, LangChain, AutoGen, CrewAI, Semantic Kernel, or equivalent technologies.
Develop LLM-powered applications using models such as OpenAI GPT, Azure OpenAI, Anthropic Claude, Gemini, or equivalent.
Implement RAG (Retrieval-Augmented Generation) pipelines using vector databases and enterprise knowledge sources.
Develop intelligent agents with tool/function calling, API integrations, memory, planning, workflow orchestration, and human-in-the-loop capabilities.
Build and integrate MCP (Model Context Protocol) servers/tools and other agent tool ecosystems where applicable.
Design multi-agent architectures for complex enterprise use cases involving specialized agents and coordinated workflows.
Develop prompt engineering strategies, structured outputs, guardrails, and evaluation mechanisms to improve agent accuracy and reliability.
Integrate AI agents with enterprise applications, REST APIs, databases, SaaS platforms, and internal systems.
Implement AI observability, tracing, evaluation, monitoring, and governance for production agentic systems.
Optimize LLM applications for latency, scalability, reliability, security, and cost.
Develop automated testing and evaluation frameworks for LLM and agent performance.
Deploy AI solutions using cloud platforms such as Microsoft Azure, AWS, or Google Cloud.
Collaborate with data scientists, ML engineers, software engineers, architects, and business stakeholders to deliver AI solutions.
Required Skills
Agentic AI & Generative AI
Strong hands-on experience in Agentic AI and Generative AI.
Experience designing AI agents, autonomous workflows, and multi-agent systems.
Strong understanding of LLMs, transformer architectures, embeddings, context windows, and inference.
Experience with RAG, prompt engineering, function/tool calling, AI memory, and agent orchestration.
Experience with LangGraph / LangChain / AutoGen / CrewAI / Semantic Kernel or similar frameworks.
Knowledge of MCP and agent-tool integration is highly desirable.
Programming & AI/ML
Strong programming skills in Python.
Experience with APIs, microservices, REST, JSON, and event-driven architectures.
Good understanding of Machine Learning and NLP concepts.
Experience with model evaluation, experimentation, and performance optimization.
Data & Vector Technologies
Experience with vector databases such as Azure AI Search, Pinecone, Weaviate, Milvus, Chroma, or FAISS.
Experience working with SQL/NoSQL databases.
Understanding of document processing, chunking, embeddings, metadata, and retrieval strategies.
Cloud & Deployment
Hands-on experience with Microsoft Azure / AWS / GCP.
Experience with services such as Azure OpenAI, Azure AI Search, Azure AI Foundry, Azure Functions, AKS, or equivalent cloud services.
Knowledge of Docker, Kubernetes, CI/CD, Git, and cloud deployment.
Experience deploying AI/LLM applications into production environments.
Preferred Qualifications
Experience building enterprise-grade Agentic AI solutions.
Experience with multi-agent orchestration and complex workflow automation.
Knowledge of AI safety, responsible AI, security, data privacy, and LLM guardrails.
Experience with LLM observability/evaluation platforms such as LangSmith, Arize, Phoenix, or equivalent.
Knowledge of MLOps/LLMOps practices.
Experience fine-tuning or adapting LLMs is a plus.
Strong problem-solving and analytical skills.
Excellent communication and stakeholder-management skills.
Education
Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
Key Technologies
Python | Generative AI | Agentic AI | LLMs | RAG | LangGraph | LangChain | MCP | Multi-Agent Systems | Prompt Engineering | Function Calling | Vector Databases | Azure OpenAI | Azure AI Foundry | OpenAI | Docker | Kubernetes | REST APIs | MLOps/LLMOps

Work arrangement
No