- 6+ years of relevant hands-on technical experience implementing, and developing cloud ML solutions on AWS.
- Hands-on experience on AWS Machine Learning services. Proven experience using AWS Sagemaker leveraging different types of data sources, Training jobs, real-time and batch Inference, and Processing Jobs.
- Good Experience developing applications using LLMs with Langchain.
- Must have experience using GenAI frameworks such as vertexAI, OpenAI, AWS Bedrock.
- Must have Hands-on experience fine-tuning large language models( LLM) and Generative AI (GAI), specifically LLama2.
- Must have Hands-on experience working with (Retrieval Augmented Generation) RAG architecture and experience using vector indexing such as Opensearch, Elasticsearch.
- Strong familiarity with higher-level trends in LLMs and open-source platforms.
- Should have experience with Deep Learning Concepts. Transformers, BERT, Attention models
- Prompt Engineering: Engineer prompts and optimize few-shot techniques to enhance LLMs performance on specific tasks, eg personalized recommendations.
- Model Evaluation Optimization: Evaluate LLMs zero-shot and few-shot capabilities, fine-tuning hyperparameters, ensuring task generalization, and exploring model interpretability for robust web app integration.
- Response Quality:



Collaborate with ML and Integration engineers to leverage LLMs pre-trained potential, delivering contextually appropriate responses in a user-friendly web app.
- Implement and manage MLOps principles and best practices for Gen AI models
- Thorough understanding of NLP techniques for text representation and modeling
- Able to effectively design software architecture as required
- Experience with at least one of the workflow orchestration tools, Airflow, StepFunctions, SageMaker Pipelines, Kubeflow etcKnowledge of a variety of machine learning techniques (Supervised/unsupervised etc) (clustering, decision tree learning, artificial neural networks, etc) and their real-world advantages/drawbacks
- Ability to create end to end solution architecture for model training, deployment and retraining using native AWS services such as Sagemaker, Lambda functions, etc
- Ability to collaborate with cross-functional teams such as Developers, QA, Project Managers, and other stakeholders to understand their requirements and implement solutions.

Positive to have skills:

- Experience of working for customers/workloads in the Edtech domain with use cases.
- Experience with software development

Skills: Nlp, MLops, Airflow

Experience: 6.00-9.00 Years

📌 Associate Architect - Machine Learning (AWS) (Thiruvananthapuram)
🏢 Quantiphi Analytics Solutions
📍 Thiruvananthapuram