AI solutions fail when teams treat an LLM demo as a production system. I design AI platforms that retrieve trusted knowledge, coordinate multi-step agents, safely call business tools, and produce observable, permission-controlled outcomes.
My core focus includes:
• Agentic AI systems using LangGraph, LangChain, CrewAI and AutoGen
• RAG and Graph RAG with hybrid retrieval, reranking and grounded responses
• MCP servers and API tools connecting AI with CRMs, EHRs, ERPs and internal systems
• Voice AI using real-time STT/TTS, telephony, WebSockets, SIP/WebRTC and LLM orchestration
• OpenAI API, Anthropic Claude, Google Gemini, AWS Bedrock and self-hosted LLM integrations
• Python/FastAPI backends, PostgreSQL, pgvector, Pinecone, Qdrant, Docker and cloud deployment
• Structured outputs, Pydantic/JSON-schema validation, RBAC, audit logs and human approval controls
• LLM evaluation and observability covering groundedness, hallucinations, drift, latency, token cost and tool reliability
My delivery experience spans enterprise AI assistants, multi-agent sales and operations automation, real-time voice agents, healthcare documentation and EHR integrations, financial research and document intelligence, medical NLP, customer-service automation, multimodal content workflows, private knowledge systems and AI-enabled SaaS products.
Across these engagements, I have worked with structured and unstructured data ingestion, RAG and Graph RAG, MCP and API tool execution, speech-to-text and text-to-speech, workflow orchestration, model and provider integration, multi-tenant architecture, role-based permissions, auditability, evaluation, monitoring, cloud deployment and human-in-the-loop controls. This allows me to support both focused AI features and larger platforms that connect multiple models, data sources, users, and operational systems.
I can support a focused AI feature, an MVP, or the architecture and delivery of a larger enterprise platform. My priority is not simply connecting an API; it is building the validation, permissions, monitoring, and operational controls required for dependable production use.
Upwork record: $500K+ earned | 17,700+ hours | 100% Job Success | Top Rated Plus
Send me your target workflow, available data sources, and required actions, and I will help identify the right architecture and delivery path.
Dheeraj S. earns an estimated $6.5k/mo. That's 4.5× the typical freelancer and more than 99.69% of everyone we track.