₹10L – ₹25L a year estimatetypical for IT Jobs in India, 834 posts
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Role at a glance
Experience
2+ years
Education
Bachelor's degree
Must-have skills
CI/CDDockerGitMicroservicesMicrosoft AzurePython
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Job description
Location: Hyderabad, Chennai, Noida, India
tech carrot is a leading global IT service provider and partner in digital transformation offering cost-effective, customer-centric, and cutting-edge digital solutions to clients worldwide. founded in 2016, tech carrot is headquartered in Dubai, UAE. Being a people-oriented organization with a philosophy of "We care" as our core DNA, tech carrot continually focuses on new initiatives and strategies to increase overall employee experience by providing a plethora of platforms to drive employee development, overall efficiency, increase overall employee satisfaction. With over 200+ technology specialists who are engaged and motivated we are growing at 20% a year-on-year since inception. We offer cutting-edge services across multiple industries and deliver seamless customer experiences, ensuring high value and rapid growth for our clients. tech carrot has earned wide recognition as a reliable partner for organizations across the globe because of its professional approach and strong delivery capabilities. The Middle East and Africa are handled by UAE operations, while APAC is handled by India operations. We have also expanded in the USA and the Netherlands as well. We are seeking a hands-on AI Engineer to design, build, integrate and deploy intelligent AI agents for conversational and autonomous experiences. The role spans agent design, orchestration, tool integrations, evaluation, production deployment and continuous optimization. You will work closely with AI product managers, data scientists, backend engineers and business stakeholders to turn requirements into scalable AI solutions. Key Responsibilities and Skills AI Agent Development & Orchestration
Design and deploy LLM-powered agents that reason, plan, retrieve information and execute actions.
Build multi-agent systems with orchestrators, supervisors and specialized agents; implement tool calling, handoffs, delegation and multi-step execution.
Integrate OpenAI, Azure OpenAI, Anthropic and other LLM providers.
Deep understanding of speech models and their different architectures
Support Arabic and English experiences.
Optimize accuracy, latency, reliability and token costs. RAG and Semantic Retrieval
Develop system prompts, structured outputs and RAG solutions grounded in enterprise data.
Build KB ingestion and retrieval pipelines
Hands on experience with vector databases including pinecone and open search Tool Development & Enterprise Integrations
Build REST APIs, webhooks and Model Context Protocol (MCP) integrations for enterprise systems.
Implement secure authentication, authorization and session handling.
Enable business actions such as product discovery, recommendations, bookings and transactions. Agent Evaluation, Testing & Optimization
Build automated evaluations for task completion, tool selection, accuracy and response quality.
Maintain test datasets for edge cases and multi-turn conversations; run regression tests.
Debug execution traces, tool calls and context propagation; monitor production quality, latency and costs. Production Deployment & Engineering Excellence
Build maintainable production services with logging, monitoring, observability and tracing.
Support CI/CD, code reviews, unit tests, documentation and secure development.
Troubleshoot production incidents and integration or performance issues. Technical Skills
Strong hands-on Python, backend/API development and asynchronous programming.
Proven delivery of LLM applications and AI agents, using at least one of LangGraph/LangChain, Kore.ai, OpenAI Agents SDK, Semantic Kernel or CrewAI.
Strong understanding of orchestration, tool calling, context, memory and structured LLM workflows.
Experience with REST APIs, microservices, RAG, embeddings and vector databases such as Pinecone or Azure AI Search.
Working knowledge of Git, Docker, CI/CD, cloud deployment and LLM debugging.
Voice and text conversational AI, STT/TTS and real-time architectures. Requirements Professional Experience 2+ years of software or AI engineering, including at least one production-grade agentic AI or LLM application. Experience integrating AI solutions with enterprise systems and external APIs. Strong independent problem solving and end-to-end delivery ownership. Bachelor’s or Master’s degree in Computer Science, Software Engineering, AI or a related field. Preferred Qualifications Kore.ai Artemis or a similar enterprise agent platform. MCP server/client development and agent-to-agent communication. Azure cloud and enterprise AI deployment. LLM evaluation and observability tools such as LangSmith or Langfuse. Ideal Candidate A builder, not just an AI enthusiast: someone who has personally written agent code, implemented tools, debugged LLM behavior and deployed AI applications. Comfortable owning orchestration, APIs, context management and production troubleshooting.
This guide is for freshers in India preparing for DevOps, cloud or backend roles, and for experienced developers and SREs facing a Docker and Kubernetes round at a Pune IT services firm or a Bengaluru product company. It has 60 questions in three levels: easy questions on images, containers, Dockerfiles, Compose and the core Kubernetes objects; medium questions on multi-stage builds, probes, resources, autoscaling, rollouts, Helm and debugging failing pods; and hard questions on internals, scheduling, security, zero-downtime releases and cluster design. Versions are named wherever behaviour changed. Try each file and command on your laptop or a local cluster, then explain the answer aloud.
Questions you may be asked
What is a container, and how is it different from a virtual machine?
What is the difference between a Docker image and a container?
Walk through this Dockerfile. What does each instruction do?
What is the difference between CMD and ENTRYPOINT in a Dockerfile?