Agentic AI Engineer (5-10 years of skilled experience)

Role Description

Roles & Responsibilities

- 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.
- 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.
- 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 understanding of agentic AI concepts, including planning, reasoning, routing, tool/function calling, memory, and multi-step workflow orchestration.
- 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 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.
- Familiarity with relational databases, vector databases, knowledge graphs, and enterprise search technologies.
- 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.
- 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-Agen

📌 Agentic Ai Engineer - Assistant Manager (Bengaluru)
🏢 State Street
📍 Bengaluru