Technical Architect ML

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About Quantiphi:

Quantiphi is an award-winning AI-First digital engineering and consulting company focused on delivering high-impact Services and Solutions that help organizations solve what truly matters. We partner with enterprises to reimagine their businesses through intelligent scalable and transformative AI driving measurable outcomes at the very core of their operations. Since our founding in 2013 Quantiphi has tackled some of the worlds most complex business challenges by combining deep industry expertise disciplined cloud and data engineering practices and cutting-edge applied AI research. Our work is rooted in delivering accelerated quantifiable business value not just technology for technologys sake. Headquartered in Boston Quantiphi is a global organization with 4000 professionals serving clients across key industry verticals including BFSI Healthcare & Life Sciences CPG MFG TME etc. As an Elite and Premier partner to leading cloud and AI platforms such as NVIDIA Google Cloud AWS and Snowflake we build and deliver enterprise-grade AI services and solutions that create real-world impact. Weve been recognized with:

17x Google Cloud Partner of the Year awards in the last 8 years.

3x AWS AI/ML award wins.

3x NVIDIA Partner of the Year titles.

2x Snowflake Partner of the Year awards.

We have also garnered top analyst recognitions from Gartner ISG and Everest Group.

We offer first-in-class industry solutions across Healthcare Financial Services Consumer Goods Manufacturing and more powered by cutting-edge Generative AI and Agentic AI accelerators.

We have been certified as a Great Place to Work for the third year in a row-. Be part of a trailblazing team thats shaping the future of AI ML and cloud innovation. Your next big opportunity starts here! For more details visit: Website or LinkedIn Page.

Role:

Architect Machine Learning Engineer

Experience: 812 Years

Location: New Jersey (Onsite)

Job Summary: We are seeking an experienced Architect Machine Learning Engineer to architect build and deploy production-grade agentic AI systems and multi-agent workflows from the ground up. The ideal candidate will have deep expertise in designing autonomous AI systems that can collaborate reason and execute complex tasks with minimal human intervention. You will be responsible for creating scalable robust agentic workflows using cutting-edge frameworks like CrewAI/Langraph while ensuring enterprise-grade deployment on major cloud platforms.

Roles & Responsibilities:

Architect & Build Agentic Systems: Design and develop end-to-end multi-agent systems from scratch. You will create the foundational agent harnesses define communication protocols and build orchestration layers using frameworks like CrewAI Langgraph and AutoGen. Architectural decisions to ensure:

Hierarchical and collaborative multi-agent structures with well-defined agent roles responsibilities and communication protocols

Dynamic task decomposition sophisticated tool integration planning mechanisms (ReAct) and self-correction loops

Develop state management systems and memory mechanisms for persistent agent interactions

Engineer Advanced Agent Capabilities: Develop custom agent-tools and define specialized agent-skills that empower agents to perform complex domain-specific tasks.

Pioneer Context Engineering: Implement advanced context engineering and memory systems to ensure agents maintain state learn from interactions and make informed decisions in dynamic environments.

Deploy Production-Grade Solutions: Own the deployment scaling and maintenance of robust low-latency agentic systems on major cloud platforms (GCP AWS or Azure). You will implement best-in-class MLOps practices for monitoring continuous integration/continuous deployment (CI/CD) and system reliability.

Integrate and Optimize LLMs: Integrate LLMs to serve as the core reasoning engines for autonomous agents. You will apply advanced techniques like RAG and PEFT to optimize performance.

Create and maintain comprehensive tool libraries for agents including API integrations database queries and external service connections

Design and implement RAG systems using vector databases (Pinecone Weaviate ChromaDB)

Develop custom tools and plugins that enable agents to interact with various enterprise systems and APIs

Ensure tool reliability error handling and seamless integration within agentic workflows

Implement comprehensive monitoring and tracing systems for agent behavior performance cost optimization and latency analysis Design novel evaluation frameworks to assess multi-step agentic task success reliability and accuracy

Utilize advanced observability tools (LangSmith Arize AI or custom solutions) to trace agent decision making processes

Establish metrics and KPIs for measuring agentic system performance in production environments

Required Skills & Qualifications:

Experience:

6-8 years of hands on experience in machine learning and AI engineering with proven track record of taking ML systems to production

Demonstrated expertise in building multi-agent systems and agentic workflows preferably with Langraph/CrewAI Technical Skills - Must Have:

Programming & ML: Expert-level Python proficiency with ML frameworks (TensorFlow PyTorch Transformers). Experience with FastAPI async programming and microservices architecture

Data & Vector Systems: Hands-on experience with vector databases (Pinecone Weaviate ChromaDB) and building scalable RAG systems

Monitoring & Observability: Experience with LLM application monitoring tools (LangSmith Weights & Biases custom telemetry solutions)

Proven ability to architect and implement complex AI systems from scratch in production environments

Cloud Platform Expertise: Production-level experience with at least one major cloud platform (AWS GCP or Azure) including: Compute services (EC2 GCE Azure VMs) Serverless functions (Lambda Cloud Functions Azure Functions) Container orchestration (EKS GKE AKS) Managed AI/ML services (SageMaker Vertex AI Azure ML)

Production & DevOps: Strong skills in Infrastructure as Code (Terraform CloudFormation) CI/CD pipelines (GitHub Actions Jenkins) and containerization (Docker Kubernetes)

Technical Skills -

Good to have:

Experience with prompt engineering techniques fine-tuning SLMs (PEFT SFT RLHF) and model optimization

Knowledge of distributed systems message queues and event-driven architectures for agent coordination

Familiarity with SDLC best practices version control (Git) and agile development methodologies

Experience with tool-calling agents multi-step workflows and stateful orchestration (e.g. graphs planners routers).

Hands-on evals for agents: trajectory / tool-use checks golden traces LLM-as-judge with fixed rubrics regression suites.

Online evals drift thinking and clear quality gates before or after deploy (thresholds alerts rollback criteria).

Safety and abuse: prompt injection via tools untrusted retrieval PII handling in prompts and logs allowlists and guardrails.

Cost and latency discipline: budgets per run timeouts caps on turns and tool calls.

Model lifecycle: routing / gateway patterns version pinning fallbacks and which model for which step.

Memory and state: what is persisted retention redaction and what must never be stored