Associate Architect - Machine Learning Engineer

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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!

About Phi Labs

Phi Labs is the innovation and research division of Quantiphi, focused on advancing next-generation AI technologies and translating cutting-edge research into scalable enterprise solutions. The team works on emerging areas including Generative AI, autonomous AI agents, multimodal intelligence, and AI platform innovation.

Role Overview :

We are looking for a Machine Learning Solution Architect who will operate at the intersection of AI research, advanced prototyping, and scalable architecture design.

This role will focus on exploring, experimenting, and productionizing cutting-edge AI techniques, particularly in Generative AI and Agentic AI systems. The architect will work closely with research scientists, applied ML engineers, and platform teams to design novel AI capabilities, validate them through rapid experimentation, and evolve them into reusable AI platforms and frameworks.

Key Responsibilities :

AI Research & Innovation

• Explore and evaluate state-of-the-art research in Generative AI, LLM architectures, and autonomous AI agents.
   

• Prototype novel AI capabilities and system designs based on emerging research.
   

• Contribute to internal research initiatives, whitepapers, and innovation accelerators.
   

• Translate academic or open research ideas into enterprise-ready AI architectures.

• Ability to interpret and implement research papers across multiple domains, including Life Sciences and Digital Twins.
   

• Strong understanding of DevOps practices such as containerization, scalable deployments, and production-ready infrastructure.
   

• Up-to-date with recent advancements and trends in Generative AI.

Generative AI & Agentic AI Systems :

• Design architectures for agentic AI systems capable of planning, reasoning, and tool usage.
   

• Build experimental frameworks for :

  • Multi-agent collaboration
     

  • Autonomous decision-making workflows
     

  • LLM reasoning and planning systems
     

  • Retrieval-augmented AI systems
     

• Explore innovations in AI memory systems, tool orchestration, and knowledge grounding.
   

Rapid Prototyping & Experimental Platforms

• Lead development of research prototypes and experimental AI platforms.
   

• Develop reference architectures for GenAI applications such as:
   

  • AI copilots
     

  • AI research assistants
     

  • Autonomous workflow agents
     

• Run benchmarks and evaluations for new models, architectures, and agent frameworks.
   

AI Platform & Architecture Design

• Define modular AI architectures that support experimentation and rapid iteration.
   

• Architect internal platforms that enable fast development of GenAI and agent-based systems.
   

• Apply platform engineering principles to build reusable AI components including:
   

  • agent orchestration layers
     

  • prompt management systems
     

  • evaluation frameworks
     

  • experimentation infrastructure.
     

Collaboration with Research & Engineering Teams

• Work closely with  ML engineers to convert research concepts into working prototypes.
   

• Provide technical leadership for AI architecture and system design decisions.
   

• Guide engineering teams in building scalable implementations of experimental AI systems.

Required Qualifications :

• 5-7+ years of experience in Machine Learning, AI engineering, or AI system architecture
   

• Strong experience with deep learning and large language models
   

• Experience building LLM-based applications or frameworks
   

• Solid programming expertise in Python
   

• Experience designing distributed AI systems
   

• Strong understanding of ML system architecture and product ionization
  
   

Preferred Experience :

• Experience with agent frameworks and autonomous AI systems
   

• Experience building RAG pipelines and knowledge-driven AI systems
   

• Familiarity with research papers and experimental AI frameworks
   

• Experience with AI evaluation, benchmarking, and model experimentation
   

• Experience with cloud-native architectures and platform engineering

Technologies & Areas of Exploration :
 

Candidates should have familiarity or interest in areas such as:

• LLM ecosystems
   

• Autonomous AI agents
   

• AI orchestration frameworks
   

• Vector databases and knowledge retrieval
   

• Multimodal AI systems
   

• AI reasoning and planning architectures