Location: Mumbai (Work From Office)

About Quantiphi

Quantiphi is an award-winning AI-first digital engineering company driven by the desire to solve transformational business problems. By combining deep industry expertise, cloud and data engineering, and cutting-edge AI research, Quantiphi helps customers build smarter products, frictionless customer experiences, autonomous processes, and safer businesses.

As an Architect – Machine Learning Engineer, you will design and develop advanced machine learning models and algorithms to solve complex business problems. You will optimize and deploy these models on AWS infrastructure, ensuring scalability and reliability.

Must Have Skills

- 7+ years of hands-on experience implementing and developing cloud ML solutions on AWS.
- Strong experience with AWS SageMaker, including:
- Training Jobs
- Processing Jobs
- Batch & Real-time Inference
- Working with multiple data sources
- Strong NLP expertise:
- Deep Learning concepts (Transformers, BERT, Attention Models)
- Python
- Hugging Face Transformers
- SpaCy
- NLTK
- Stanford NLP
- NLP concepts including tokenization, embeddings, syntactic & semantic parsing, Named Entity Recognition (NER), and coreference resolution.
- Experience building Agentic AI applications:
- LangChain
- Amazon Bedrock Agents
- Autonomous task planning and multi-step reasoning




- Experience architecting AI solutions using AWS services:
- AWS Lambda
- Amazon Bedrock
- Step Functions
- S3
- API Gateway
- SageMaker
- Experience implementing Model Context Protocol (MCP) for state, memory, context window, and prompt orchestration.
- Experience integrating agentic workflows with LLMs such as:
- Titan
- Nova
- Cohere
- Claude
- Hands-on experience fine-tuning Large Language Models (LLMs), specifically Llama 2.
- Familiarity with:
- Prompt Engineering
- Tool Calling
- Vector Databases (OpenSearch, Pinecone, Elasticsearch, Bedrock Knowledge Bases)
- Context Management
- Model evaluation and optimization:
- Zero-shot/Few-shot evaluation
- Hyperparameter tuning
- Model interpretability
- Experience with workflow orchestration tools such as:
- Airflow
- AWS Step Functions
- SageMaker Pipelines
- Kubeflow
- Experience building secure, scalable APIs and integrating third-party data sources.
- Strong collaboration skills with Developers, QA, Product Managers, and cross-functional stakeholders.

Positive to Have

- Experience working on EdTech use cases.
- Software development experience.

Skills:- Machine Learning (ML), Generative AI, AWS CloudFormation, Amazon Web Services (AWS) and AWS Bedrock

📌 Associate/Architect ML Engineer (AWS) (Mumbai)
🏢 Quantiphi
📍 Mumbai