Agentic AI Developer
New
J
JobgetherAI Engineering
Remote-friendly working model with flexibility across India.Full-TimeMiddle
Salary2,500,000 - 4,000,000 INR per year
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Job Details
- Experience
- 3–7 years
- Required Skills
- DockerPythonLangChain
Requirements
- 3–7 years of professional experience in software engineering, AI development, machine learning, or a closely related field.
- Hands-on experience with generative AI or agentic systems.
- Advanced proficiency in Python, including asynchronous programming and the development of scalable AI services.
- Strong practical knowledge of major LLM provider APIs, such as OpenAI, Anthropic, Mistral, or equivalent platforms.
- Hands-on experience developing agentic applications using LangChain, LangGraph, AutoGen, CrewAI, or similar agent orchestration frameworks.
- Understanding of multi-agent architectures, tool calling, workflow orchestration, prompt design, and context management.
- Working knowledge of vector databases such as Pinecone, Qdrant, or Weaviate, and relational databases.
- Experience with Docker and containerized application development.
- Strong understanding of software engineering fundamentals, including modular architecture, unit testing, and version control.
- Experience establishing rigorous testing and evaluation methodologies for AI systems.
- Bachelor's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or a related technical discipline.
Responsibilities
- Design, develop, and optimize autonomous AI agents capable of reasoning, planning, reflection, decision-making, and multi-step task execution.
- Build multi-agent architectures and state-based workflows using frameworks such as LangGraph, AutoGen, CrewAI, or comparable custom orchestration platforms.
- Develop reliable agent loops that enable LLMs to select tools, execute actions, evaluate results, and adapt their approach when required.
- Integrate enterprise databases, internal applications, and external APIs with AI agents while implementing appropriate authentication, security boundaries, schema validation, and error handling.
- Design short-term and long-term memory mechanisms that allow agents to maintain relevant context throughout extended workflows and interactions.
- Develop automated evaluation frameworks to assess agent performance, reliability, task completion, reasoning quality, and failure scenarios.
- Monitor and optimize token consumption, latency, execution costs, and overall system performance.
- Implement safety mechanisms and guardrails to reduce hallucinations, constrain unintended actions, and prevent unsafe or uncontrolled agent behavior.
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