AI Architech

Deploys in customer clouds — including managed LLMs in-VPC, so customer data never leaves their environment.

Requirements

• Senior or staff-level applied AI/ML experience. You have shipped production AI systems, not just prototypes, and have led the technical direction on at least one of them.
• Deep fluency in modern LLM patterns. Hands-on experience with retrieval-augmented generation (RAG), tool-using agents, and structured output (e.g., JSON schemas, function calling).
• Agent orchestration. You have built multi-step agent workflows with LangGraph or a similar framework, including verification steps, fallbacks, and points where a human confirms before the system proceeds.
• Systems architecture. You can design an engine, its data interfaces, and the agent layer on top as one coherent, maintainable system, and document decisions (e.g., architecture decision records).
• Grounding, hallucination control, and evaluation. You have made "no unsupported answers" a hard rule in a real system, and measured faithfulness and hallucination with automated evaluations, ideally running in CI.
• Determinism on top of probabilistic models. You know how to make the same question against the same data return the same answer, and can explain the techniques you used.
• Clear judgment about what a model can legitimately claim. You understand why computed, traceable answers matter more than plausible-sounding ones in a high-stakes domain.
• Production software engineering in Python. You write robust, tested services with SQL access to warehouses or databases (e.g., Snowflake, PostgreSQL), and you are comfortable with containerized deployment.
• Structured data and retrieval tooling. Working knowledge of vector stores (pgvector, Pinecone, Weaviate, or Qdrant) and of computing reliable answers over structured data.
• Technical leadership. You mentor engineers, run design reviews, and turn strategy set by US leadership into well-built execution.
• Strong communication. You can explain architecture trade-offs to engineers, product managers, and non-technical stakeholders in clear English.
• Location and working hours. Based in Hyderabad (preferred) or remote elsewhere, and able to keep a few hours of daily overlap with US time zones.
• Flexible engagement. Open to full-time, contract, or contract-to-hire, whichever suits you best.

Nice-to-Haves

• Finance, accounting, or other audited domains. Experience where outputs face auditors or regulators (finance, healthcare, insurance, legal).
• Knowledge graphs and reasoning engines. Experience with knowledge graphs, semantic layers, or rule/reasoning engines that model facts and relationships.
• Human-in-the-loop systems. You have designed review, confirmation, or feedback loops that improve a system over time.
• Managed LLMs inside customer clouds. Hands-on with Amazon Bedrock, Google Vertex AI, or Azure OpenAI deployed in a customer's private network (VPC), so data never leaves their environment.
• Multi-cloud and on-prem deployment. Experience shipping AI to AWS, Azure, and GCP, including single-tenant and on-premises installs, and working with platform teams on model serving and inference cost.
• AI observability. Experience with tracing and evaluation tooling for LLM applications.