What are the responsibilities and job description for the ML Engineer position at KONNECTINGTREE INC?
Fulltime/Contract
Role Overview
The ML Engineer – LLM Platforms & Assistants will design, build, and operate production-grade large language model (LLM) pipelines primarily within AWS-based environments. This role focuses on integrating OpenAI models into modular Python services, implementing Retrieval-Augmented Generation (RAG) and semantic search, and deploying scalable, secure, and observable AI assistants.
Key Responsibilities
- Design and maintain LLM integrations using OpenAI APIs within AWS environments.
- Build Python-based LLM services deployed on AWS compute platforms (ECS, EKS, Lambda, or EC2).
- Implement RAG workflows and semantic search using AWS data and storage services.
- Develop LangChain or agentic workflows supporting reasoning and tool use.
- Integrate LLM pipelines with ETL/ELT workflows and enterprise data systems.
- Deploy and integrate MCP servers and emerging orchestration tools.
- Apply AWS security best practices using IAM, KMS, and Secrets Manager.
- Implement monitoring and observability using CloudWatch and related tools.
- Migrate custom GPT solutions into production-grade AWS-hosted assistants.
Ideal Candidate Profile
- 4–7 years in software, ML, or applied AI engineering.
- Strong Python proficiency.
- Experience with RAG and semantic search.
- Hands-on OpenAI LLM integration experience.
- Experience deploying systems in AWS (S3, Lambda, ECS/EKS, IAM).
Preferred Qualifications
- Experience with Amazon Bedrock.
- AWS data services: Glue, Athena, OpenSearch, Aurora.
- ETL/ELT pipeline development.
- LLM orchestration and reasoning workflows.
- Benchmarking and LLM evaluation.
- Infrastructure as Code (CDK, CloudFormation, Terraform).
Education:
Bachelor’s degree in Computer Science, Engineering, Information Systems, or equivalent experience.