About the RoleWe are seeking a Senior Agentic AI Engineer to design and build production AI agents that can reason over enterprise data, retrieve supporting evidence, use tools safely, and complete multi-step workflows with clear audit trails. You will work with Data Science, Backend Engineering, Product, and client stakeholders to turn complex operational decisions into reliable agent systems. You will also support the cloud deployments and operational infrastructure setup in AWS.

Key Responsibilities

- Agent architecture and orchestration: Design agentic workflows with explicit state, structured outputs, tool routing, etc., and support deployment and maintenance in a cloud environment.
- MCP and tool integration: Develop secure MCP servers, function tools, APIs, and SDKs that connect agents to databases, document stores, billing platforms, and workflow systems. Define typed contracts, authorization boundaries, validation, idempotency, and actionable error handling.
- Retrieval and knowledge systems: Create RAG pipelines for payer policies, coding rules, prior-authorization requirements, historical resolutions, and internal procedures.
- Human review and controlled autonomy: Implement confidence- and risk-based routing, approval checkpoints, reviewer feedback, reversible actions, and clear escalation paths.
- Agent evaluation and testing: Build representative datasets and replay tests from historical cases. Measure tool-selection accuracy, groundedness, decision and action correctness, policy adherence, latency, and cost; add regression gates for prompts, models, tools, and workflow changes.
- Security and governance: Protect sensitive data through least-privilege tool access,



scoped credentials, prompt-injection defenses, input and output validation, PHI and PII controls, traceable decisions, and secure handling of untrusted documents.
- Monitoring & Observability: Set up comprehensive logging, metrics, and alerting using systems like CloudWatch, Prometheus, Grafana, or Elasticsearch to track query accuracy, latency, data drift, and system reliability.
- Production application engineering: Build reliable Python and TypeScript services, asynchronous jobs, event-driven integrations, and model-provider abstractions. Package and deploy services with practical logging, tracing, versioning, and rollback support.

Required Skills and Experience
- Experience: 5 or more years in software, backend, or AI engineering, including hands-on experience building production LLM or agentic applications.
- Languages & Core Tech: Strong proficiency in Python, SQL, and REST/gRPC service architectures.
- Agent frameworks: Experience with n8n, LangGraph, LangChain, CrewAI, OpenAI Agents SDK, or a comparable orchestration framework.
- AI platforms and models: Experience with AWS Bedrock, OpenAI, Anthropic Claude, Hugging Face, and open-source models.
- MCP and tool calling: Direct experience designing or integrating MCP clients and servers, function tools, structured outputs,



and secure tool authorization.
- RAG and data systems: Experience with embeddings, metadata-aware retrieval, vector databases, relational databases such as SQL server, and NoSQL databases.
- Quality engineering: Ability to test nondeterministic systems using evaluation datasets, deterministic validators, mocks, integration tests, and failure-mode analysis.
- Secure system design: Experience with authentication, authorization, secrets, encryption, tenant or data isolation, and secure API and database integration.
- Delivery fundamentals: Working knowledge of Docker, Git-based delivery, and CI/CD pipelines in cloud infrastructure.

Good to Have
- Healthcare revenue cycle knowledge: Experience with medical claims, X12 837 and 835 transactions, CMS-1500, CARC and RARC codes, prior authorization, coordination of perks, coding edits, payer policies, and claim denial appeals.
- Confidential and identity technologies: Experience with trusted execution environments and privacy-preserving processing of regulated data.
- Cloud Infrastructure (AWS): Hands-on experience across AWS services, including IAM, Secrets Manager, VPC, ECS/EKS, Lambda, S3, and CloudWatch.
- Developer platforms: Experience building reusable SDKs, maintaining open-source packages, or designing stable integration interfaces for other engineering teams.
- Workflow and data tooling: Exposure to n8n, Airflow, Prefect, S3, Glue, Athena, or related workflow and data platforms.
- Regulated environments: Familiarity with HIPAA, SOC 2, GDPR, audit requirements, and controls for production systems that process sensitive enterprise data.

📌 Sr Agentic AI Engineer (Mumbai)
🏢 MindWise Technologies
📍 Mumbai