Agentic AI Engineer (5–10 years of professional experience)

Role Description

We are seeking an Agentic AI Engineer with a strong combination of applied data science and AI engineering skills to design, develop, evaluate, and productionize enterprise-grade agentic AI applications. The role requires hands-on expertise in Python, machine learning, Generative AI/LLMs, prompt engineering, tool integration, and cloud-based deployment.

The Agentic AI Engineer will build intelligent applications capable of planning, reasoning, routing, retrieving information, invoking tools, and executing multi-step workflows. The role will work closely with data scientists, AI/ML engineers, software engineers, data engineers, and platform teams to translate business requirements into secure, reliable, scalable, and measurable agentic AI solutions.

Roles & Responsibilities

• Design and develop agentic AI applications using patterns such as planning, routing, reflection, tool/function calling, memory, and multi-agent collaboration.
• Implement AI/ML and Generative AI solutions using Python, with an emphasis on quality, reliability, efficiency, and scalability.
• Develop orchestration workflows that connect LLMs with enterprise APIs, databases, retrieval systems, and other tools.
• Design and optimize prompts, system instructions, tool descriptions, routing logic, and context-management strategies.
• Build RAG-based solutions, including document processing, chunking, embedding, retrieval, reranking, grounding, and context optimization.
• Apply data science techniques to agent development, including problem framing, hypothesis design, experimentation, error analysis, and iterative model improvement.
• Define and implement evaluation frameworks for agentic AI systems, covering task completion, answer correctness, faithfulness, tool-selection accuracy, trajectory quality, latency, cost, safety, and reliability.
• Create representative evaluation datasets, test scenarios, error taxonomies, regression suites, and human-evaluation workflows.

• Partner with data scientists to evaluate model choices, prompt strategies, retrieval approaches, and fine-tuning requirements where applicable.
• Collaborate with software and platform engineers to productionize agentic applications through APIs, SDKs, containers, and CI/CD pipelines.
• Work with data engineers to develop data pipelines, preprocessing utilities, data-quality checks, and feature or context engineering workflows.
• Integrate observability for agent execution, including prompts, responses, tool calls, intermediate steps, errors, token consumption, latency, and quality metrics.
• Use cloud platforms, preferably Azure or AWS, to deploy and scale AI models, agents, and supporting services.
• Establish engineering best practices for modularity, testing, reproducibility, versioning, documentation, security, and model governance.
• Troubleshoot and optimize agent behavior, model performance, retrieval quality, response latency, token usage, and infrastructure cost.
• Engage with business and technology stakeholders to gather requirements and translate them into measurable AI objectives and technical solutions.
• Conduct design and code reviews and provide technical guidance and mentorship to less experienced team members.
• Stay current with advancements in agentic AI, Generative AI, machine learning, and orchestration frameworks, and recommend pragmatic adoption based on enterprise requirements.

Core/Must have skills

• BS, B.Tech, or Master’s degree in Computer Science, Computer Information Systems, Engineering, Mathematics, Data Science, or a related field.
• 5–10 years of professional experience, including strong hands-on experience in Python-based AI/ML or software development.
• Strong Python programming skills and experience building modular, testable, and production-quality applications.
• Practical experience developing applications using LLMs and Generative AI models, such as GPT, Claude, Llama, or BERT-class models.
• Strong understanding of agentic AI concepts, including planning, reasoning, routing, tool/function calling, memory, and multi-step workflow orchestration.
• Experience with at least one agent or LLM application framework, such as LangChain, LangGraph, Semantic Kernel, AutoGen, LlamaIndex, or an equivalent framework.
• Proficiency in prompt engineering, prompt optimization, structured outputs, context management, and model interaction patterns.
• Hands-on experience building RAG solutions using embeddings, vector search, retrieval, reranking, and grounding techniques.
• Good knowledge of machine learning algorithms, experimentation techniques, evaluation methodologies, and error analysis.
• Experience defining and executing evaluation approaches for LLM or agentic systems, including repeatable testing and regression validation.
• Experience integrating AI applications with APIs, databases, enterprise services, and external tools.
• Experience partnering with engineering teams to deploy AI/ML solutions using APIs, containers, CI/CD, monitoring, and cloud infrastructure.
• Hands-on experience with Azure or AWS cloud platforms, including relevant AI/ML services and development tooling.
• Understanding of software engineering fundamentals, including Git, unit testing, API design, debugging, and secure coding practices.
• Excellent communication, stakeholder-management, and cross-functional collaboration skills.

Good to have skills

• Experience with multi-agent architectures, agent-to-agent communication, human-in-the-loop workflows, and long-running agent processes.
• Experience with Azure AI Foundry, Azure OpenAI, AWS Bedrock, Databricks, or Azure Machine Learning.
• Familiarity with Model Context Protocol (MCP) or similar standards for connecting agents with tools and enterprise data sources.
• Experience with fine-tuning, model adaptation, model routing, or selecting models based on quality, latency, and cost requirements.
• Knowledge of LLMOps and AgentOps practices, including prompt and model versioning, experiment tracking, evaluation pipelines, and operational monitoring.
• Experience with Docker, Kubernetes, and Helm for deployment and management of AI applications.
• Familiarity with relational databases, vector databases, knowledge graphs, and enterprise search technologies.
• Experience with asynchronous processing, event-driven architectures, and distributed application patterns.
• Understanding of enterprise security and Responsible AI controls, including identity and access management, PII protection, prompt-injection defenses, least-privilege tool access, auditability, and data privacy.
• Familiarity with model risk management, compliance requirements, and governance considerations for enterprise GenAI applications.
• Experience working in regulated industries or with enterprise-scale AI platforms.

Work Schedule

On-premise

Keywords (If any)

Agentic AI Engineer, Generative AI, LLM, Python, Applied Machine Learning, Data Science, AI Engineering, AI Agents, Multi-Agent Systems, Tool Calling, Prompt Engineering, RAG, Agent Evaluation, LLMOps, AgentOps, Docker, Kubernetes

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