AI Developer - Agentic AI

Role

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

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.