About Quantiphi:
Quantiphi is an award-winning, AI-First global digital engineering company that helps the world’s leading Fortune 1000 organizations transform bold ideas into measurable business impact. We go beyond building innovative AI technologies - we solve the problems that matter most to our clients.
Since our founding in 2013, Quantiphi has built a proven track record of turning complex challenges into meaningful outcomes across industries.
Headquartered in Boston, with more than 4,000 professionals worldwide, we partner with global enterprises to deliver large-scale digital, cloud, and AI-driven transformation. #SolvingWhatMatters
We are an Elite and Premier partner to Google Cloud, AWS, NVIDIA, Snowflake, and other leading technology platforms, and our work has been recognized across the industry, including:
- 21 Google Cloud Partner of the Year awards in the past 10 years
- 3 AWS AI/ML Partner of the Year awards
- 3 NVIDIA Partner of the Year awards
- 3 Snowflake Partner of the Year awards
- Rated Leaders by Gartner, Forrester, IDC, ISG, Everest Group and other leading analyst firms
Quantiphi delivers First-in-class AI solutions across Life Sciences, Healthcare, Banking, Financial Services, CPG, Manufacturing, Energy, High-Tech, Telecommunications, etc., powered by cutting-edge Generative AI and Agentic AI accelerators.
We are also proud to be certified as a Great Place to Work - reflecting our commitment to our people and our culture.
For more details, visit: Website or LinkedIn Page
Job Description:
Required Skills:
- 8+ years of experience in Machine Learning, AI, Software Engineering, Data Science, or Solution Architecture.
- Strong experience designing and implementing enterprise-scale AI/ML architectures.
- 3+ years of hands-on experience with Generative AI, LLMs, and production AI applications.
- Strong expertise in RAG (Retrieval-Augmented Generation), embeddings, vector databases, semantic/hybrid search, and knowledge retrieval.
- Hands-on experience with Agentic AI, AI agents, tool/function calling, orchestration, and multi-agent workflows.
- Strong proficiency in Python and experience with ML/AI frameworks such as PyTorch, TensorFlow, Scikit-learn, or Hugging Face.
- Experience with LLM platforms/models such as Azure OpenAI, OpenAI, AWS Bedrock, Anthropic, Google Gemini, Llama, or equivalent.
- Strong knowledge of prompt engineering, context engineering, fine-tuning, model evaluation, guardrails, and hallucination mitigation.
- Experience designing and implementing MLOps/LLMOps pipelines, including model deployment, monitoring, evaluation, versioning, and CI/CD.
- Strong experience with at least one major cloud platform: Microsoft Azure, AWS, or GCP.
- Experience with cloud AI/ML services such as Azure Machine Learning, Azure AI Foundry, AWS SageMaker/Bedrock, or Google Vertex AI.
- Experience with Docker, Kubernetes, APIs, microservices, CI/CD, and Infrastructure as Code.
- Strong understanding of data pipelines, data lakes/lakehouses, databases, data governance, and enterprise integration.
- Experience with AI/ML security, privacy, Responsible AI, governance, and compliance.
- Strong system-design and architecture skills, with the ability to translate business requirements into scalable technical solutions.
- Excellent communication, presentation, stakeholder-management, and technical leadership skills.
Key Responsibilities:
- Design end-to-end enterprise AI/ML architectures from data ingestion through model development, deployment, inference, monitoring, and optimization.
- Lead the architecture and implementation of Generative AI and LLM-based solutions across enterprise use cases.
- Design scalable RAG architectures, including document ingestion, chunking, embeddings, vector search, retrieval, reranking, and LLM generation.
- Architect and implement Agentic AI solutions, including AI agents, tool calling, workflow orchestration, memory, and human-in-the-loop capabilities.
- Evaluate and recommend LLMs, AI platforms, frameworks, and technologies based on performance, scalability, security, latency, and cost.
- Define architecture standards and reusable patterns for GenAI, ML, MLOps, and LLMOps.
- Lead the transition of AI/ML POCs and prototypes into production-grade solutions.
- Design and implement MLOps/LLMOps capabilities for model lifecycle management, deployment, monitoring, evaluation, and continuous improvement.
- Establish LLM evaluation and observability frameworks covering accuracy, relevance, hallucination, latency, token usage, cost, and reliability.
- Work closely with Data Scientists, ML Engineers, Software Engineers, Data Engineers, Product Managers, Security teams, and business stakeholders.
- Provide technical leadership and mentorship to AI/ML engineering teams.
- Ensure AI solutions meet enterprise requirements for security, privacy, governance, Responsible AI, and regulatory compliance.
- Identify opportunities to improve existing AI/ML platforms and optimize performance, scalability, and cloud costs.
- Create architecture diagrams, technical specifications, design documents, standards, and implementation roadmaps.
- Stay current with emerging GenAI, LLM, Agentic AI, ML, and cloud technologies and assess their applicability to business needs.
