Job Title

About the Role We are hiringtalented and motivated engineersto join ourLLM and Agentic AI Engineering team . In this role, you will work across thefull lifecycle of modern AI systems —from hands-on programming and system design to prompt engineering, agent orchestration, evaluation pipelines, and enterprise deployment. You will help buildproduction-grade agentic AI applicationsthat go beyond static copilots—systems that use tools, maintain memory, are continuously evaluated, and integrate deeply with enterprise workflows. This role is ideal for engineers who actively usenext-generation AI developer tools(e.g., Codex-style code agents, Cursor-like IDE copilots, Google’s agent workbench frameworks such as Anti-Gravity) and want to shape how these tools are operationalized in real production systems.Key Responsibilities Programming & Software Engineering (Core Responsibility) Writeclean, maintainable, and production-quality codein Python and related backend/frontend technologies. Design and implementmodular services, libraries, and APIssupporting LLM and agentic workflows. Buildscalable backend componentsfor agent execution, memory management, retrieval, and evaluation. Follow software engineering best practices includingcode reviews, testing, documentation, and CI/CD . LLM Integration, Prompting & Developer Tooling Design, test, and operationalizeLLM-powered workflowsusing modern AI developer tools and workbenches. Developrobust system prompts, task schemas, and tool interfacesoptimized for reliability and repeatability. Evaluate foundation models, prompting strategies, and tool-use patterns using structured AI workbench environments. Agentic Systems & Tool Orchestration Buildagentic workflowscapable of planning, reasoning, tool invocation, and multi-step task execution. Integrate agents withinternal APIs, databases, codebases, and enterprise systems . Designstateful agentswith explicit control over memory, retries, tool boundaries, and failure modes. Retrieval, Memory & Knowledge Systems ImplementRAG pipelinesusing vector databases (ElasticSearch, FAISS, etc.) and hybrid retrieval approaches. Designcontextual memory layers(episodic, semantic, task-level) to support long-running and adaptive agents. Optimize grounding strategies to reduce hallucinations and improve factual consistency. Data & AI Pipelines Build pipelines tocollect, clean, structure, and version datasetsused for prompting, retrieval, and evaluation. Incorporatehuman feedback and production signalsto iteratively improve agent behavior. Evaluation, Safety & Observability Implementcontinuous evaluation frameworkscovering task success, reliability, drift, and failure patterns. Instrument agents to captureprompts, tool calls, intermediate steps, and outputsfor traceability and audit. Contribute togovernance-aligned practicessuch as risk scoring, reproducibility, and audit readiness. Collaboration & Delivery Work closely withsenior engineers, product managers, and domain expertsto translate real business workflows into deployed agentic systems. Participate indesign reviews, agent behavior analysis, and iteration cycles . Continuous Learning Stay current with evolvingAI developer tooling, agent frameworks, and evaluation platformsacross OpenAI, Google, and open-source ecosystems. Track emerging best practices inenterprise AI governance(e.g., NIST AI RMF, EU AI Act).Qualifications ✅ Required ·3+ years of hands-on professional experience in software engineering, AI/ML systems, or backend/platform development. B.Tech / M.Tech inComputer Science, AI/ML, Data Science , or a related discipline from a reputed institute. Strong fundamentals insoftware engineering, machine learning, NLP, and Python-based development . Hands-on experience usingAI-assisted development tools(Codex-style agents, Cursor, or similar IDE copilots). Familiarity withembeddings, vector search, and retrieval-based systems . Ability to translate ambiguous problem statements intoworking, production-ready code . Preferred / Bonus Experience building or extendingagent frameworks and tool-based workflows . Exposure toevaluation harnesses, prompt testing frameworks, or AI workbench platforms . Understanding ofAI safety, observability, and governanceconsiderations. Experience withcontainerized or API-driven deployments(Docker, REST, CI/CD). Frontend exposure (React or similar) for building internal AI tooling or dashboards. Job Details Job Type:Full-time Benefits:Health Insurance Work Location:In-person