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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 : Snowflake Data Warehouse

Minimum 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 Snowflake, 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 banking, insurance, retail, healthcare, travel, logistics or telecom 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 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.

Develop Cortex-powered LLM applications and agents inside Snowflake security perimeter build RAG and document intelligence using Cortex Search and Cortex AI Functions integrate Streamlit/Snowpark applications apply RBAC, masking, lineage and monitoring for regulated enterprise use cases.

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.

Typically 5+ years of software/data/AI engineering experience, including 2+ years in cloud-native engineering and 1+ year in GenAI, LLM, NLP or agentic AI delivery.

Typically 7+ years of software/data/AI engineering experience, including 3+ years in cloud-native architecture/engineering and 1–2+ years in GenAI, LLM, NLP or agentic AI delivery.

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 banking, insurance, retail, healthcare, travel, logistics or telecom.

Required Skills/ Experience

Hands-on experience with Snowflake Cortex AI, Cortex Agents, Cortex Search, Cortex Analyst, Cortex AI Functions/LLM Functions, Snowpark, Streamlit in Snowflake, Dynamic Tables, Tasks, Streams, Snowflake ML, RBAC, masking policies and observability.

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

SnowPro Advanced Architect/Data Engineer or Snowflake ML exposure experience with Snowpark Python, Streamlit, semantic models, dbt, Native Apps, Snowflake governance, data sharing and cost/performance tuning.

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.