AI Application Developer

AI Application Developer Location: India, Remote

Work Experience: 4-8 Years

Requirements:

• At least 1-2 years of experience with AI/ML, LLM, RAG, Databricks, or enterprise data platform solutions preferred.
• Strong programming experience in Python.
• Experience building backend APIs using FastAPI, Flask, Django, or similar frameworks.
• Hands-on experience with Azure Databricks, Apache Spark, Delta Lake, notebooks, jobs, and workflows.
• Understanding of Unity Catalog concepts such as catalogs, schemas, tables, volumes, permissions, access controls, and lineage.
• Experience with Mosaic AI, MLflow, model serving, LLM application deployment, or AI/ML platform services.
• Knowledge of RAG, Graph RAG, Document RAG, embeddings, semantic search, chunking strategies, and prompt engineering.
• Experience with vector databases or Databricks Vector Search.
• Understanding of knowledge graphs, triples, entities, relationships, graph query patterns, and entity-centric reasoning.
• Experience integrating with LLM provider APIs through secure platform services.
• Experience with event-driven or asynchronous processing patterns.
• Experience with REST APIs, WebSockets, message streams, and API gateway integration.
• Understanding deterministic rule processing, decision workflows, exception lifecycle management, or business process automation.
• Familiarity with EDI/X12 transaction processing is preferred.
• Good understanding of observability, logging, tracing, audit events, and error handling.
• Experience with Git, CI/CD pipelines, Agile delivery, and enterprise development practices.
• Experience with healthcare, pharma, supply chain, payer, provider, drug, pricing, claims, or regulated enterprise domains.
• Experience with EDI/X12 exception handling, upstream transaction processors, or healthcare data exchange.
• Experience with agentic AI applications, tool orchestration, MCP integrations, or bounded tool execution.
• Knowledge of FDA drug data, DEA validation, NADAC/GPO pricing, EPCIS, VRS, or serialization workflows.
• Knowledge of PII redaction, prompt governance, responsible AI, human-in-the-loop approval, and auditability.
• Experience with Azure services such as Azure Key Vault, Azure Storage, Azure Event Hubs, Azure Functions, Azure DevOps, AKS, or Azure Monitor.
• Familiarity with Terraform or infrastructure-as-code.
• Exposure to Grafana, Prometheus, OpenTelemetry, or similar observability tools.

Qualifications: Bachelors or Masters degree in Computer Science, Information Technology, or a related field.

• Design and develop backend services for the AI-based Application.
• Implement operator-facing service flows supporting dashboards, exception workspaces, context inspection, and learning governance workflows.
• Develop transactional APIs for operator commands such as acknowledge, resolve, escalate, annotate, and case updates.
• Implement real-time event streaming for exception arrival, agent progress, queue updates, and re-evaluation events.
• Build and enhance EDI/X12 exception processing components including EDI Parser, exception normalization, and internal exception model mapping.
• Implement deterministic exception resolution flows including L1 reference-data lookups and L2 policy-based routing.
• Develop Agent Orchestrator workflows for L3 agentic resolution when deterministic rules cannot resolve exceptions.
• Integrate bounded resolution tools such as drug lookup, pricing check, serial verification, DEA check, and document lookup.
• Build integration with Graph RAG and Document RAG capabilities for contextual retrieval and decision support.
• Develop services such as Exception Service, Context Query Service, Learning Service, and Decision Trace Emitter.
• Implement decision trace capture for deterministic and agentic decisions to support auditability, explainability, and learning loops.
• Build ingestion and retrieval integrations using authoritative external sources such as drug registries, DEA registry, pricing data, serialization data, policies, documentation, and regulatory guidance.
• Implement feedback ingestion, pattern mining support, candidate rule synthesis support, routing optimization support, validation sandbox integration, and promotion workflows.
• Integrate application services with Azure Databricks, Unity Catalog, Mosaic AI, vector stores, graph stores, object stores, and model-serving services.
• Build REST, HTTPS, WebSocket, JSON-RPC, event-driven, and asynchronous interfaces as required.
• Implement secure access patterns using authentication, authorization, role-based access control, data governance, and audit controls.
• Ensure idempotent processing, metadata tracking, lineage, traceability, and consistency across persistence layers.
• Collaborate with QA, DevOps/MLOps, data engineering, architecture, security, and business teams.
• Participate in code reviews, design discussions, troubleshooting, performance tuning, and production readiness activities.

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