AI Developer - Agentic AI

Job Description

ob Description Role : AI Developer - Agentic AI Exp: 2-3 Years Work Mode: 12- 10 pm, Onsite( Mohali, Punjab) Job Role & Responsibilities Design, develop, and deploy Agentic AI systems capable of autonomous task execution by integrating reasoning, memory, and tool use to enable intelligent behavior across complex, multi-step workflows. Architect intelligent agents that can dynamically interact with APIs, data sources, and third-party tools to accomplish diverse objectives with minimal human intervention. Optimize performance of agentic frameworks by enhancing model accuracy, minimizing response latency, and ensuring scalability and reliability in real-world applications. Develop reusable, testable, and production-grade code , adhering to best practices in software engineering and modern AI development workflows. Collaborate with cross-functional teams , including product managers, designers, and backend engineers, to convert business requirements into modular agent behaviors. Integrate Retrieval-Augmented Generation (RAG) , advanced NLP techniques, and knowledge graph structures to improve decision-making and contextual awareness of agents. Conduct rigorous profiling, debugging, and performance testing of agent workflows to identify bottlenecks and improve runtime efficiency. Write and maintain comprehensive unit, integration, and regression tests to validate agent functionality and ensure robust system performance. Continuously enhance codebases , refactor existing modules, and adopt new design patterns to accommodate evolving agentic capabilities and improve maintainability. Implement secure, fault-tolerant, and privacy-compliant designs to ensure that deployed agentic systems meet enterprise-grade reliability and data protection standards. Qualification Required: Bachelor s degree in computer science , or related field. Specialization or Certification in AI or ML is a plus. Technical Expertise: 2+ years of hands-on experience in AI/ML/DL projects, with a strong emphasis on Natural Language Processing (NLP) , Named Entity Recognition (NER) , and Text Analytics . Proven ability to design and deploy Agentic AI systems autonomous, goal-oriented agents that exhibit reasoning, memory retention, tool use, and execution of multi-step tasks. Practical expertise in agent architecture , task decomposition, and seamless integration with external APIs, databases, and tools to enhance agent capabilities. Skilled in agent prompting strategies , including dynamic prompt chaining and context management, to guide language models through intelligent decision-making workflows. Experience with Retrieval-Augmented Generation (RAG) pipelines and generative AI , with a strong focus on optimizing NLP models for low-latency, high-accuracy production use. Solid foundation in deep learning methods , recommendation engines, and AI applications within HR or similar domains. Exposure to Reinforcement Learning (RL) frameworks and holds relevant certifications or specializations in Artificial Intelligence , showcasing continuous learning and depth in the field. Minimum skills we look for: Skills & Expertise (with Agentic AI focus) Proven experience in building Agentic AI systems , including autonomous agents capable of multi-step reasoning, memory management, and tool use. Expertise in agent design patterns , task decomposition , dynamic planning, and decision-making logic using LLMs. Skilled in integrating multi-agent coordination , goal-setting, and feedback loops to create adaptive, evolving agent behavior. Strong command over prompt engineering , contextual memory structuring , and tool calling mechanisms within LLM-powered agent workflows. Proficiency in managing agent memory (short-term, long-term, episodic) using vector databases and custom memory stores. Ability to build autonomous task execution pipelines with minimal human input, combining language models, APIs, and third-party tools. Experience with frameworks and orchestration for agent behavior tracing, logging, and failure recovery . Tools & Technologies Agentic AI Agentic Frameworks : LangChain, CrewAI, AutoGen, AutoGPT, BabyAGI for building, managing, and orchestrating intelligent agents. LLM APIs : OpenAI (GPT-4/3.5), Anthropic (Claude), Cohere, Hugging Face Transformers. Memory & Vector Databases : FAISS, Weaviate, Pinecone, Chroma for embedding-based agent memory and contextual retrieval. Prompt Management Tools : PromptLayer, LangSmith for testing, evaluating, and refining agent prompts and traces. RAG & Context Enrichment : LangChain RAG pipelines, Haystack, Milvus. Autonomy Infrastructure : Docker, FastAPI, Redis, Celery for building scalable agent runtimes. Observability : OpenTelemetry, Langfuse (or similar) for tracing agent decisions, failures, and success metrics. Testing Agentic Behavior : Integration with PyTest + mock APIs/tools to validate autonomous decision logic and fallback strategies. ", "