High Salary: LLM Engineer

2 weeks ago

Delhi, India System Soft Technologies Full time
Job Description – LLM Engineer

System Soft Technologies is a premier technology company providing exceptional consulting services and solutions that drive innovation, enhance business value, and boost competitiveness. For over 25 years, we have built trusted partnerships with our clients, helping us grow into a $200MM+ enterprise. With the collective resources of over 1,200 associates covering the full spectrum of IT expertise, we anticipate and meet the unique needs of our clients, consistently delivering exceptional quality that exceeds expectations.

Job Summary

We are seeking a skilled LLM Engineer proficient in Python programming and experienced in developing, deploying, and optimizing large language models (LLMs). The ideal candidate will have hands-on experience with FastAPI or Flask frameworks, Lang Chain implementation, and building Retrieval-Augmented Generation (RAG) pipelines. You will play a key role in integrating cutting-edge AI technologies to solve complex business problems, focusing on vector stores and retrievers while deploying scalable solutions on AWS.

Responsibilities

1. Python Development:

a. Design, develop, and maintain scalable web services using FastAPI or Flask frameworks.

b. Write efficient, reusable, and modular Python code to support API-driven LLM applications.

2. Lang Chain & Supporting Frameworks:

a. Implement Lang Chain to build custom pipelines for document indexing, retrieval, and summarization.

b. Integrate Lang Chain’s RAG capabilities with other components like vector stores and retrievers to support real-time querying and document processing.

3. RAG Pipelines:

a. Architect and deploy Retrieval-Augmented Generation (RAG) systems for chatbots, knowledge systems, and other generative AI applications.

b. Optimize RAG systems for speed, accuracy, and scalability across multiple use cases.

4. Vector Stores & Retrievers:

a. Work with vector databases like Pinecone, Chroma, FAISS, or Milvus to store and manage embeddings.

b. Implement retrievers and re-rankers to improve query efficiency, ensuring high-quality and relevant outputs for users.

5. AWS Cloud Deployment:

a. Deploy and manage LLM-based applications on AWS, leveraging services such as Lambda, EC2, S3, EKS, and RDS.

b. Ensure the scalability, availability, and reliability of deployed applications.

6. Dashboards and Monitoring (Optional):

a. Create monitoring dashboards using tools like Grafana or Tableau for real-time system monitoring, analytics, and performance insights.

7. Experimentation with Generative AI:

a. Research and integrate the latest advancements in generative AI technologies.

b. Experiment with fine-tuning and adapting large language models (like GPT, BERT) for new, innovative use cases.

Qualifications

- Python proficiency, especially with web frameworks like FastAPI or Flask.- Strong experience with Lang Chain and associated libraries.- Proven expertise in building and optimizing RAG pipelines.- Proficiency in using vector databases (e.g., Pinecone, FAISS).- Experience with retrievers and re-rankers.- Solid understanding of AWS services (Lambda, EC2, RDS, etc.).- Knowledge of SQL and NoSQL databases.- Familiarity with dashboarding tools such as Grafana and Tableau.

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