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 : Google Cloud Data Services

Minimum 7.5 Year(s) Of Experience Is Required

Educational Qualification : 15 years full time education

Role Summary / Description

AI Powered Tech Talent

Engineer role in AI LLM Technology Architecture. Hands-on engineering role focused on designing, building, integrating, testing and operationalizing enterprise-grade LLM, GenAI and agentic AI components across active client engagements.

Own platform-specific engineering on GCP, translating high-level architecture into working, production-quality components for LLM-driven applications, RAG pipelines, multi-agent workflows and AI platform integrations.

Bring practical industry experience in financial services, healthcare, retail, manufacturing, telecom or media to identify domain data, process constraints, controls and adoption risks while designing GenAI solutions that are safe, scalable and relevant.

Operate as a hands-on technical lead or engineering lead, contributing code, design decisions, reusable patterns and engineering documentation.

Key Responsibilities

Design and build LLM application components including prompts, tools, agents, orchestration flows, memory/context handling, retrieval pipelines and evaluation harnesses.

Build Gemini-powered LLM applications and agents implement grounding/RAG with BigQuery, Vertex AI Search and vector stores orchestrate tool calling and event-driven workflows evaluate model output quality and deploy AI services on Cloud Run or GKE.

Implement data ingestion, parsing, chunking, enrichment, embeddings, vector search and retrieval workflows for structured and unstructured enterprise content.

Engineer safety and control components including PII detection/redaction, prompt-injection defenses, content filters, guardrails, authentication, authorization, lineage and audit logging.

Collaborate with architects, data engineers, product owners and security stakeholders to convert solution designs into tested, observable and maintainable software components.

Maintain technical artifacts such as component designs, integration specifications, deployment runbooks, evaluation results and reusable engineering patterns.

Required Qualifications

Bachelor s degree in Computer Science, Computer Engineering, Data Science, AI/ML, Information Technology or a related engineering discipline.

Hands-on coding experience in Python and strong understanding of APIs, distributed systems, CI/CD, testing, observability and secure SDLC practices.

Experience delivering AI/ML or data products in at least one industry domain such as financial services, healthcare, retail, manufacturing, telecom or media.

Required Skills/ Experience

Hands-on experience with Vertex AI / Gemini Enterprise Agent Platform, Gemini models, Model Garden, Agent Builder/Agent Engine patterns, Vertex AI Search, BigQuery, AlloyDB/Cloud SQL vector support, Cloud Run, GKE, Pub/Sub, Cloud Functions, IAM, Cloud Logging and Monitoring.

Strong understanding of LLM application architecture patterns including RAG, function/tool calling, agent orchestration, model invocation, prompt engineering, embeddings, vector databases and evaluation metrics.

Ability to implement traditional ML and GenAI components across ingestion, feature/data preparation, model integration, deployment, monitoring and continuous improvement.

Practical knowledge of security, privacy, governance, performance, scalability, reliability and cost controls for production AI systems.

Experience with Git-based development, automated testing, CI/CD pipelines, infrastructure-as-code and agile delivery in client-facing environments.

Good to Have Skills

Google Cloud Professional Data Engineer, Professional ML Engineer or Cloud Architect certification experience with BigQuery-based semantic layers, Dataform/dbt, LangChain/LlamaIndex, GKE ML workloads and MLOps on Vertex AI.

Exposure to open-source frameworks such as LangChain, LangGraph, LlamaIndex, Haystack, MLflow, FastAPI, Docker and Kubernetes.

Experience with Responsible AI, model risk management, synthetic data generation, human-in-the-loop review, A/B testing and GenAI cost optimization.