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 experience required: 7.5 years
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 protected, 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.
- 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.
- 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.

Locations: Job No. ATCI-5701028-S2061678 | Hyderabad
Required Skill: Large Language Models (LLMs)

📌 Large Language Model Architect (Secunderabad)
🏢 Accenture
📍 Secunderabad