Project Role : Large Language Model Architect

Project Role Description : Architect large language models (LLM) that can process and generate natural language. Design neural network parameters, trained on large quantities of unlabeled text data.

Must have skills : Large Language Models (LLMs)

Good to have skills : Databricks Unified Data Analytics Platform

Minimum 12 Year(s) Of Experience Is Required

Educational Qualification : 15 years full time education

Role Summary / Description

AI Powered Tech Talent

Experienced and senior AI/LLM Technology Architecture Engineer, responsible for designing and delivering end-to-end AI platform architectures on Databricks.
Own the technical architecture for modern AI systems spanning classical machine learning, generative AI, LLM applications, RAG, agentic workflows and enterprise AI platform integration.
Act as the technical authority for one or more architecture domains such as agentic application design, AI security and trust, AI operations and observability, data and knowledge engineering, model platforms and inference.
Bring practical industry experience in financial services, healthcare, manufacturing, retail, telecom or life sciences to shape domain-grounded solutions, define controls, and ensure the AI architecture aligns with real business processes and enterprise standards.

Key Responsibilities

Translate business strategy and product goals into a technical vision, architecture blueprint, non-functional requirements and implementation roadmap.
Lead stakeholder workshops to align on feasibility, project scope, solution boundaries, delivery dependencies and client-facing expectations.
For Databricks, define lakehouse-centric LLM architecture design governed RAG and agent systems using Delta Lake, Unity Catalog and Vector Search define MLflow evaluation and tracing standards establish deployment architecture through Model Serving, Workflows and monitoring for auditable, scalable GenAI products.
Architect model and tool agnostic multi-agent systems, including orchestration, tool use, agent memory, context management, MCP/control-plane patterns and reusable service abstractions.
Design the end-to-end data and context layer including ingestion, preprocessing, synchronization, chunking, embeddings, vector search, knowledge graphs and semantic retrieval for reliable RAG.
Define evaluation frameworks for accuracy, relevance, faithfulness, groundedness, latency, cost, safety, security and operational reliability.
Establish AI security, governance and observability as centrally enforced design controls including guardrails, prompt-injection defense, PII protection, access control, audit logging and OpenTelemetry-style tracing.
Maintain architecture decision records, component diagrams, sequence diagrams, design specifications, integration patterns and reusable reference architecture assets.

Required Qualifications

Bachelor's degree or equivalent in Computer Science, Computer Engineering, Data Science, AI/ML, Information Technology or a related engineering discipline.
Minimum 10+ years of experience in software engineering, data engineering, AI/ML engineering or technology architecture.
Minimum 5+ years of experience designing and deploying enterprise-grade advanced AI or cloud data solutions using at least one cloud vendor.
Minimum 2+ years of experience in agentic AI, LLM and generative AI solution architecture or engineering delivery.
Minimum 4+ years of coding experience using Python and experience with APIs, distributed systems, reusable frameworks and cloud-native application patterns.
Minimum 4+ years of experience in ML, deep learning, NLP, data engineering, analytical engineering or AI product delivery.
Demonstrated experience as a solution/technology architect in industry contexts such as financial services, healthcare, manufacturing, retail, telecom or life sciences.

Required Skills/ Experience

Hands-on architecture and engineering experience with Databricks Mosaic AI, Model Serving, Agent Framework, MLflow tracing/evaluation, Vector Search, Unity Catalog, Delta Lake, Delta Live Tables, Lakehouse Monitoring, Feature Store, Databricks Workflows, Jobs and Model Training.
Strong knowledge of LLM architecture patterns including RAG, embeddings, vector databases, prompt engineering, model routing, fine-tuning/adaptation, function calling, tool integration and agent orchestration.
Ability to define enterprise AI platform patterns for performance, scalability, security, reliability, observability, governance, cost optimization and operational support.
Experience designing reusable agent services, memory services, API gateways, integration adapters, orchestration layers, evaluation harnesses and deployment pipelines.
Experience with CI/CD, infrastructure-as-code, automated testing, model evaluation, MLOps/LLMOps, monitoring and production release governance.
Strong stakeholder management skills with ability to communicate architecture trade-offs, risks and recommendations to engineering, product, security and leadership teams.

Good to Have Skills

Databricks Machine Learning Professional, Data Engineer Professional or Generative AI certification experience with Spark/PySpark, Unity Catalog governance, Delta Live Tables, MLflow, LangGraph/LangChain, model fine-tuning and lakehouse cost/performance optimization.
Exposure to open-source AI and orchestration frameworks such as LangChain, LangGraph, LlamaIndex, Haystack, Semantic Kernel, MLflow, FastAPI, Docker and Kubernetes.
Experience with responsible AI, model risk management, AI governance boards, red-teaming, human-in-the-loop review, A/B testing and GenAI FinOps.
Experience building reusable enterprise reference architectures, estimation models, accelerators, playbooks and architecture governance frameworks.