While technology is the heart of our business, a global and diverse culture is the heart of our success. We love our people and we take pride in catering them to a culture built on transparency, diversity, integrity, learning and growth.
If working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi!
JOB ROLE - Senior Machine Learning Engineer (4-7 yrs)
As a Senior Machine Learning Engineer at Quantiphi, you will be responsible for designing and developing advanced machine learning models and algorithms to solve complex business problems. You will work on optimizing and deploying these models on AWS infrastructure, ensuring scalability and reliability.
Must have skills:
Experience with LangChain, LangGraph, LlamaIndex, CrewAI, or similar Agentic AI frameworks.
Python: Good exposure to Python (Pandas, NumPy, FastAPI, advanced Python concepts).
AWS Bedrock: Hands-on experience with AWS Bedrock and foundation models such as Claude Haiku and Claude Sonnet.
LLM & GenAI: Hands-on experience in developing RAG pipelines, Prompt Engineering, and LLM-based GenAI applications.
ML Pipeline Architecture: Experience with Titan Embeddings, Amazon OpenSearch Vector Search, and vector-based retrieval.
Agentic AI: Hands-on experience with Agentic AI frameworks and AWS Bedrock AgentCore for developing AI agents and workflow orchestration.
AI Agents & Knowledge Base: Experience developing AI agents for customer query automation and Knowledge Base (KB) solutions.
Experience with document parsing, chunking, vectorizing and re-ranking strategies.
Guardrails & Security: Experience configuring AWS Bedrock Guardrails and implementing authentication and authorization for secure AI applications.
AWS Services: Hands-on experience with API Gateway, Lambda, S3, IAM, CloudWatch, ECR, and SageMaker.
Software Engineering: Experience with Git, REST APIs, and CI/CD pipelines.
Relevant AWS certifications (e.g., AWS Certified AI Practitioner, AWS Certified Machine Learning Engineer – Associate, or AWS Certified Solutions Architect – Associate) are a plus.
Good to have skills:
Hands-on experience with OCR/Document Intelligence engines and NLP techniques for extracting structured information from documents and images.
Hands-on experience with Bedrock Agentcore
Hands-on experience with OpenAI, Anthropic, Gemini, or other foundation models.
Experience with Docker, Kubernetes, and MLOps practices.
Experience with Redshift, SQL, DynamoDB, or AWS Glue.
Exposure to model monitoring, evaluation, and observability for GenAI applications.
Ability to work in an Agile and DevOps environment.
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!
Skills Required
- 4–7 years of relevant experience
- Experience with LangChain, LangGraph, LlamaIndex, CrewAI, or similar agentic AI frameworks
- Python experience, including Pandas, NumPy, FastAPI, and advanced Python concepts
- Hands-on AWS Bedrock experience with foundation models such as Claude Haiku and Claude Sonnet
- Experience developing RAG pipelines, prompt engineering, and LLM-based generative AI applications
- Experience with Titan Embeddings, Amazon OpenSearch Vector Search, and vector-based retrieval
- Hands-on experience developing AI agents and workflow orchestration with agentic AI frameworks and AWS Bedrock AgentCore
- Experience developing AI agents for customer query automation and knowledge-base solutions
- Experience with document parsing, chunking, vectorization, and re-ranking strategies
- Experience configuring AWS Bedrock Guardrails and implementing authentication and authorization
- Hands-on experience with AWS API Gateway, Lambda, S3, IAM, CloudWatch, ECR, and SageMaker
- Experience with Git, REST APIs, and CI/CD pipelines
- Relevant AWS certification, such as AWS Certified AI Practitioner, AWS Certified Machine Learning Engineer Associate, or AWS Certified Solutions Architect Associate
- Experience with OCR or document intelligence engines and NLP techniques
- Experience with OpenAI, Anthropic, Gemini, or other foundation models
- Experience with Docker, Kubernetes, and MLOps practices
- Experience with Redshift, SQL, DynamoDB, or AWS Glue
- Exposure to model monitoring, evaluation, and observability for generative AI applications
- Ability to work in an Agile and DevOps environment
Quantiphi Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Quantiphi and has not been reviewed or approved by Quantiphi.
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Wellbeing & Lifestyle Benefits — Wellbeing initiatives such as monthly meeting-free AMA-Zen Days, health check-ups, and wellness counseling are designed to reduce burnout and support day-to-day balance. Broader wellness programs reinforce both physical and mental health.
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Flexible Benefits — Remote/hybrid options with flexible working hours provide meaningful autonomy over where and when work gets done. Flexible leave constructs, including sabbaticals and special day leaves, add practical adaptability to the package.
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Parental & Family Support — Paid parental leave in the U.S., alongside maternity and childcare support, signals solid backing for families. These family-oriented policies integrate with a wider health and wellness focus.
Quantiphi Insights
What We Do
Quantiphi is an award-winning AI-first digital engineering company driven by the desire to solve transformational problems at the heart of business. Quantiphi solves the toughest and complex business problems by combining deep industry experience, disciplined cloud, and data-engineering practices, and cutting-edge artificial intelligence research to achieve quantifiable business impact at unprecedented speed.
