The Agentic AI Engineer is responsible for designing, building, and deploying intelligent, AI agents capable of reasoning, planning, and executing complex tasks across business and technical domains. This role focuses on production‑grade agentic systems powered by Large Language Models (LLMs), integrated with enterprise tools, APIs, and data sources. This is a hands-on role requiring end‑to‑end ownership — from agent architecture and development through deployment, monitoring, evaluation, and continuous optimization.

The ideal candidate combines strong software engineering fundamentals with deep expertise in LLM application design, agent frameworks, Retrieval‑Augmented Generation (RAG), memory systems, and multi‑agent orchestration .

In addition to strong engineering skills, this role requires a product mindset—partnering with business stakeholders to shape use cases, select appropriate agentic tools and frameworks, and translate user needs into scalable technical solutions ..

Key Responsibilit ies

Agent Architecture & Develop mentDesign and implement agentic AI architectures, includ ing:ReAct (Reason + Act)Plan‑and‑Exe cuteTool‑using ag entsMulti‑agent systems and coordina tors

Build LLM‑powered applications using modern foundation m odelsTranslate business use cases into reliable, scalable agent workflows with clear handoffs and fall backs

LLM & RAG Engin eeringDevelop and optimize prompt engineering strategies for reasoning, task execution, and too l use.Implement Retrieval‑Augmented Generation (RAG) pipelines using structured and unstructured data.Design and manage agent memory systems, incl uding:Short‑term (conversation/

state)Long‑term (vector‑based m emory)Work with vector dat abases

Tooling,



APIs & Inte grationBuild and integrate internal and external tools (APIs, microservices, enterprise sy stems).Enable agents to safely interac t with:Da tabasesBusiness appli cationsAutomation pl atformsCloud s ervicesEnsure robust error handling, retries, and fallback logic.

Production Readiness & Op erationsDeploy agentic systems into production envir onments.Implement monitoring, logging, and evaluation framewo rks for:Accuracy and task successLatency and cost ef ficiencySafety and policy co mplianceDesign and enforce guardrails to prevent hallucinations, unsafe actions, or data leakage.Continuously optimize system performance and cost

Collaboration &

DeliveryPartner closely with business stakeholders, application developers, data teams, and business stak eholders.Act as a technical product owner for agentic solutions: collect user and business requirements, clarify problem statements, and translate them into system designs and implementat ion plansIdentify high‑impact AI opportunities and rapidly prototype, validate, and scale s olutions.Contribute to internal best practices, architecture standards, and AI go vernance.Lead agentic tooling decisions: evaluate frameworks, model providers, orchestration patterns, and integration approaches; make pragmatic build/buy choices aligned to security, reliability, and cost con straints.Act as a technical product partner: collect user requirements, define success metrics and acceptance criteria,



translate needs into solution designs and implementation plans, and drive delivery from prototype to pr oduction.

Required Qual ificationsBachelor’s degree in Computer Science, Computer Engineering, Information Technology, or equivalent e xperience.3+ years of professional software development e xperience.Solid proficiency in Python (

required).Hands‑on experience working with LLM API s, and MCPSolid underst anding of:Prompt e ngineeringEmbeddings and seman tic searchRetrieval‑Augmented Genera tion (RAG)API and backend sys tem designExperience integrating AI systems into production app lications.Familiarity with cloud platforms (Azure, AWS

, or GCP).Strong problem‑solving skills and ability to manage multiple priorities in fast‑paced env ironments.Excellent written and verbal communication skills in English

Preferred / Nice‑to‑

Have SkillsExperience with agent frameworks (e.g., LangChain, Semantic Kernel, AutoGe n, CrewAI).Knowledge of multi‑agent coordinatio n patterns.Experience with evaluation frameworks for LLMs and agents.Exposure to security, privacy, and compliance considerations in AI systems.Familiarity with CI/CD pipelines and DevOps practices.Experience building AI solutions for enterprise or internal busines s use cases

What Succes s Looks LikeFull‑time ro le: on‑site.Collaboration with global or cross‑functional teams may require flexible wo rking hours.Strong emphasis on ownership, experimentation, a nd delivery.

Work

Environment:Works in a standard office environment utilizing standard offi ce equipment.Works in team and individual environments.May work weekends and overtime wh en necessary.Ability to travel as needed, both domestic and international

📌 Agentic AI Engineer (Maharashtra)
🏢 Yash Technologies
📍 Maharashtra