Senior Agentic AI Engineer

• *Production AI Agents, Enterprise RAG, Python, and Azure/AWS**

• *Mandatory Screening Criteria** :Top PriorityCandidates **must meet all of the following requirements**:

• **10\+ years of overall software engineering experience**, with strong hands\-on coding and system\-design capability.
• **Minimum 7 years of AI/ML experience**, including recent, practical experience in Generative AI, LLMs, RAG, and Agentic AI solutions.
• **10\+ years of product development experience**, with ownership across design, development, testing, deployment, production support, and enhancement.
• Proven experience working as an **individual contributor\-**able to independently design, develop, test, deploy, and troubleshoot production\-grade AI applications.
• Prior experience in **banking, financial services, fintech, payments, or other regulated financial environments** is mandatory.
• **Immediate joiners or candidates with very short notice periods** are preferred.
• Hands\-on experience using **GitHub Copilot** as part of the software\-development lifecycle, including code development, test generation, code review support, documentation, and productivity improvement.
• Hands\-on experience integrating Agentic AI solutions with enterprise tools and APIs, including **Splunk, Grafana, bank internal applications**, databases, document repositories, and workflow platforms.
• Experience building tool\-enabled agents using secure **API/function calling**, authentication, access controls, audit logs, error handling, and human approval workflows.
• Strong expertise in **Fast RAG and Graph RAG** architectures, including hybrid retrieval, reranking, metadata filtering, knowledge graphs, semantic search, and context\-grounded responses.
• Practical experience in **LLM token optimisation**, including prompt/context optimisation, intelligent chunking, retrieval filtering, model selection, caching, response streaming, latency optimisation, and cost monitoring.
• Experience with **LLM harness engineering**—designing reusable prompt templates, structured\-output schemas, evaluation datasets, automated test harnesses, regression testing, and quality metrics for LLM/agent workflows.
• Knowledge of **Model Context Protocol (MCP)** for securely connecting AI agents to enterprise tools, data sources, and APIs through governed, reusable tool interfaces.

• *Role Overview**

We are looking for a hands\-on Senior Agentic AI Engineer to design, build, and deploy enterprise AI solutions that go beyond chatbots and prompt\-based applications. The ideal candidate will have proven experience developing **stateful, tool\-enabled agents**, supported by strong Python engineering, retrieval\-augmented generation (RAG), evaluation, and cloud deployment skills.This is an engineering\-focused role requiring ownership from architecture and implementation through testing, production deployment, and continuous improvement.

• *Key Responsibilities**

• Design and implement multi\-step agent workflows involving planning, tool selection, API execution, decision\-making, and response validation.
• Build agent orchestration using **LangGraph or equivalent frameworks**, including state management, conversational memory, conditional routing, retries, and human\-in\-the\-loop approvals.
• Integrate agents with enterprise REST APIs, databases, document repositories, and business applications using secure tool/function calling.
• Develop enterprise RAG pipelines covering document ingestion, semantic chunking, embeddings, hybrid retrieval, metadata filtering, reranking, and grounded responses with citations.
• Implement evaluation frameworks for agent task completion, tool\-call correctness, retrieval quality, faithfulness, hallucinations, latency, and cost.
• Develop production\-ready Python services using FastAPI, Docker, automated testing, CI/CD, logging, and monitoring.
• Apply security controls for prompt injection, sensitive\-data handling, access permissions, and safe execution of agent actions.
• Collaborate with business stakeholders to translate requirements into reliable AI workflows with measurable outcomes.Role OverviewWe are seeking a hands\-on Senior Agentic AI Engineer / AI Developer to build secure, scalable, production\-grade AI solutions for banking and financial\-services use cases.
• The role requires an experienced individual contributor who can independently translate business requirements into reliable Agentic AI products.
• The successful candidate must have delivered solutions beyond basic chatbots or prompt\-based applications, including **multi\-step AI agents, tool/API calling, state management, RAG, evaluation frameworks, guardrails, and enterprise deployment**.
• **Key Responsibilities:**
• Independently design, develop, test, deploy, and maintain Agentic AI applications for banking and financial\-services use cases.
• Build multi\-step AI agents that can plan tasks, call approved APIs/tools, use structured outputs, retain workflow state, manage failures, and escalate actions for human approval.
• Develop agent orchestration workflows using **LangGraph, LangChain, or equivalent frameworks**, including memory, conditional routing, retries, error handling, and auditability.
• Build enterprise\-grade RAG platforms for policies, procedures, product documents, regulatory content, operational manuals, and knowledge repositories.
• Implement document ingestion, OCR/content extraction, chunking, embeddings, vector search, hybrid retrieval, reranking, metadata filtering, and response citations.
• Integrate LLM solutions securely with enterprise REST APIs, databases, workflow systems, document stores, and business applications.
• Develop Python\-based backend services and APIs using FastAPI or equivalent frameworks.
• Use **GitHub Copilot** effectively and responsibly to improve engineering productivity, code quality, documentation, test coverage, and code\-review efficiency.
• Implement LLM and agent evaluation frameworks covering task completion, answer relevance, retrieval quality, faithfulness, hallucination detection, tool\-call accuracy, latency, and cost.
• Apply AI security and governance controls, including prompt\-injection protection, PII/data masking, access control, permission\-aware tool calling, audit logging, and human\-in\-the\-loop approvals.
• Deploy containerised AI services using Docker, CI/CD pipelines, logging, monitoring, alerting, and performance optimisation.
• Work closely with product owners, architects, security teams, risk/compliance teams, and business stakeholders to deliver measurable outcomes.
• **AI, Generative AI, and Agentic AI**
• Minimum 7 years of practical AI/ML experience, with substantial recent experience in Generative AI and LLM application development.
• Demonstrable experience building **production\-grade Agentic AI solutions**, not only chatbots or RAG\-based Q\&A applications.
• Hands\-on experience with:
• LLMs, prompt engineering, structured outputs, and function/tool calling
• LangGraph, LangChain, or equivalent agent orchestration frameworks
• Agent state, conversational memory, workflow persistence, conditional routing, retries, and failure handling
• Human\-in\-the\-loop approvals and escalation workflows
• Single\-agent and multi\-agent architecture patterns
• RAG, vector databases, semantic search, hybrid search, metadata filtering, and reranking
• LLM evaluation, LLM\-as\-a\-Judge, hallucination detection, faithfulness testing, and human evaluation

• *Cloud, Deployment, and MLOps**

• Experience with **Azure OpenAI, OpenAI APIs, AWS Bedrock, or equivalent enterprise LLM platforms**.
• Strong experience with Azure or AWS cloud services, preferably including secure enterprise deployments.
• Experience developing REST APIs using FastAPI, Flask, or equivalent frameworks.
• Experience with Docker, containerisation, CI/CD, environment management, logging, monitoring, and production support.
• Experience with vector databases such as Azure Cosmos DB Vector Search, Azure AI Search, Pinecone, Weaviate, FAISS, Elasticsearch, or equivalent.
• Experience with Databricks, MLflow, Unity Catalog, model governance, or comparable MLOps platforms is preferred.

• *Banking and Financial Services Requirements**

• Mandatory experience delivering technology, AI, analytics, automation, or data solutions for **banking, financial services, fintech, payments, insurance, lending, treasury, fraud, risk, compliance, or regulatory domains**.
• Understanding of regulated\-environment requirements, including data confidentiality, PII protection, audit trails, access controls, governance, model risk, and secure API integration.
• Experience with banking use cases such as customer service, operations automation, compliance, KYC/AML, fraud detection, risk assessment, document intelligence, financial\-product knowledge, or internal employee assistants is highly desirable.

Candidate Submission RequirementsCandidates should provide a brief description of at least one production Agentic AI solution they personally developed, including:

• **Architecture:** Agent workflow, orchestration framework, tools/APIs, RAG components, state/memory design, and individual contribution.
• **Reliability and security:** Guardrails, permissions, failure handling, human approvals, logging, evaluation approach, and compliance controls.
• **Production delivery:** Cloud platform, deployment approach, CI/CD, user scale, and measurable business, quality, latency, or cost outcomes.
• **Banking/financial experience:** Relevant client, project type, use case, and responsibilities, without disclosing confidential information.

Pay: ₹2,000,000\.00 \- ₹4,000,000\.00 per year

Benefits

• Health insurance
• Paid sick time
• Provident Fund

Experience

• artificial intelligence: 7 years (Required)
• Software development: 10 years (Required)
• agentic AI: 7 years (Required)
• Product development: 10 years (Required)

Location

• Hyderabad, Telangana 500050 (Required)

Shift availability

• Day Shift (Required)
• Overnight Shift (Required)

Work Location: In person