Agentic AI Engineer

Agentic AI Engineer
Department: AI Solutions 

Location: NCR, Pune, Bengaluru 

Experience: 5+ Years

About the Role
We are seeking a highly skilled Agentic AI Engineer to design, build, test, and orchestrate autonomous AI agent systems capable of executing complex, multi-step workflows. The ideal candidate will have deep expertise in Large Language Models (LLMs), agent orchestration frameworks, retrieval systems, and enterprise-grade AI deployments.
You will work at the forefront of AI innovation, building production-ready agentic systems that leverage reasoning, planning, tool usage, memory, and human-in-the-loop interactions to solve real-world business challenges.
 

Core Areas of Expertise
● LLM Orchestration
● Agent Design & Architecture
● Multi-Agent Systems
● Tool Integration & Automation
● Prompt & Context Engineering
● RAG & Knowledge Systems
● AI Safety & Governance
 

Key Responsibilities
● Design and develop autonomous AI agents and multi-agent systems with memory, tool usage, fallback strategies, and reasoning capabilities.
● Build production-grade agent workflows using frameworks such as LangGraph, AutoGen, CrewAI, Semantic Kernel, or custom orchestration layers.
● Implement advanced agent reasoning patterns including ReAct, Plan-and-Execute, Reflection Loops, and Tool-Augmented Generation.
● Develop prompt engineering and context optimization strategies to improve reliability, accuracy, and consistency of LLM outputs.
● Design and manage orchestration workflows involving task routing, parallel execution, state management, retries, escalation paths, and human-in-the-loop interventions.
● Build comprehensive evaluation frameworks including automated testing, adversarial testing, benchmarking, and LLM performance monitoring.
● Implement Retrieval-Augmented Generation (RAG) architectures using vector databases, embeddings, semantic search, and knowledge graphs.
● Enable observability by tracing agent decisions, tool interactions, execution paths, and LLM calls using monitoring and analytics platforms.
● Apply AI security and governance best practices including prompt injection prevention, access control, tool authorization, and safe execution policies.
● Optimize agent systems for scalability, latency, throughput, and operational cost.
● Develop CI/CD pipelines for AI agents, including version control, testing automation, deployment strategies, and rollback mechanisms.
● Integrate AI agents with enterprise applications, databases, APIs, and business workflows.
● Design feedback-driven learning mechanisms using production telemetry, evaluation signals, and user feedback.
● Leverage Model Context Protocol (MCP) frameworks to enable secure tool orchestration, API integration, and database connectivity.
● Fine-tune and adapt foundation models using techniques such as LoRA, QLoRA, PEFT, RLHF, instruction tuning, and supervised fine-tuning.
● Stay current with advancements in agentic AI research, frameworks, tooling, and deployment best practices.
 

Required Skills & Experience
● 5+ years of overall software/AI engineering experience.
● Minimum 3 years of hands-on experience building and deploying LLM-based or agentic AI systems in production environments.
● Strong expertise in agentic architecture patterns including ReAct, Reflection, Plan-and-Execute, Tool Calling, and Autonomous Workflow Design.
● Advanced proficiency in Python and modern AI development ecosystems.
● Hands-on experience with one or more frameworks such as LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel, or equivalent.
● Strong understanding of prompt engineering, context window optimization, structured outputs, and LLM orchestration.
● Experience integrating AI agents with external tools, REST APIs, SQL databases, vector databases, code interpreters, and enterprise applications.
● Expertise in Retrieval-Augmented Generation (RAG), embeddings, semantic search, vector stores, and knowledge retrieval systems.
● Experience designing evaluation frameworks using RAGAS, custom evaluation harnesses, LLM-as-a-Judge methodologies, and benchmarking tools.
● Strong understanding of AI safety risks including hallucinations, prompt injection attacks, tool misuse, and mitigation techniques.
● Experience with cloud platforms such as AWS, Azure, or GCP.
● Hands-on experience with Docker, Kubernetes, and modern DevOps practices.
● Familiarity with MLOps and AIOps tools such as MLflow, Weights & Biases, LangSmith, Arize, or equivalent platforms.
● Strong experience designing conversational memory systems, semantic caching, context optimization, token management, and cost-efficient AI architectures.
 

Nice to Have
● Experience building large-scale multi-agent systems and inter-agent communication protocols.
● Exposure to metadata management, data governance, data lineage, or enterprise data catalog platforms.
● Contributions to open-source AI, LLM, or agentic AI projects.
● Experience with Java, Scala, PySpark, or COBOL.
● Knowledge of graph databases and knowledge graph implementations.
● Familiarity with AI observability and governance platforms.
 

What We Offer
● Opportunity to build cutting-edge agentic AI solutions on real-world enterprise data.
● Work alongside AI researchers, data engineers, architects, and product leaders.
● Exposure to emerging AI technologies and enterprise-scale deployments.
● Competitive compensation and benefits.
● Flexible work arrangements (Hybrid/Remote).
● A collaborative and innovation-driven work culture focused on continuous learning and