Large Language Models (LLMs)

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 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 Databricks 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 manufacturing retail telecom or life sciences 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 data-grounded agentic applications on the lakehouse implement RAG with Delta tables Vector Search and governed features use MLflow for tracing evaluation and model lifecycle deploy agents or models with Model Serving and enforce governance through Unity Catalog.
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 manufacturing retail telecom or life sciences.
Required Skills/ Experience
Hands-on experience with Databricks Mosaic AI Model Serving Agent Framework MLflow tracing/evaluation Vector Search Unity Catalog Delta Lake Lakehouse Monitoring Feature Store Databricks Workflows Jobs notebooks and Model Training.
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
Databricks Machine Learning Data Engineer or Generative AI certification experience with Spark/PySpark Delta Live Tables Unity Catalog governance LangGraph/LangChain model fine-tuning and lakehouse cost/performance optimization.
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.