Senior Machine Learning Engineer

Overview

is a place where passion changes lives. As a management consulting and technology firm focused on improving life and how we live it, we transform ideas into impact by bringing together data, science, technology and human ingenuity to deliver better outcomes for all. Here you’ll work side by side with a powerful collective of thinkers and experts shaping life-changing solutions for patients, caregivers and consumers, worldwide. ZSers drive impact by bringing a client-first mentality to each and every engagement. We partner collaboratively with our clients to develop custom solutions and technology products that create value and deliver company results across critical areas of their business. Bring your curiosity for learning, bold ideas, courage and passion to drive life-changing impact to ZS.

Technology Group

ZS's Scaled AI practice is part of ZS's rich and advanced AI ecosystem, in the Associate Consultant role, focused on creating continuous business value for clients using a range of innovative machine learning, deep learning, and engineering capabilities.

Being part of Scaled AI practice allows you to collaborate with data scientists to create state-of-the-art AI models, create and use cutting-edge ML platforms, create and deploy advanced ML pipelines and manage the complete ML lifecycle.

What you’ll do:

• Design and implement agentic AI workflows for contextual insight generation, opportunity identification, and narrative synthesis—leveraging LLM reasoning, tool use, and multi-step planning.
• Develop and optimize prompt engineering pipelines, including dynamic prompt construction, few-shot selection, chain-of-thought reasoning, and self-reflection patterns.
• Build KPI interpretation and explanation engines, translating quantitative metrics into natural language narratives with causal reasoning, driver/barrier identification, and actionable recommendations.
• Implement multimodal data processing pipelines for ingesting and analyzing unstructured content (PDFs, field notes, competitive intelligence documents, digital content) using embeddings, semantic search, and document parsing.
• Create evaluation frameworks for agent quality: measuring SQL correctness, insight relevance, narrative coherence, factual grounding, and hallucination detection.
• Design model routing and fallback strategies, selecting appropriate LLM tiers (GPT-4, GPT-3.5, Claude, domain-specific models) based on query complexity, latency requirements, and cost constraints.
• Develop vector memory and retrieval systems, building semantic indices over brand-specific knowledge (glossaries, precedent analyses, KPI definitions, best practices).
• Collaborate with Backend Engineers to productionize ML pipelines, ensuring robust error handling, monitoring, and A/B testing capabilities.
• Stay current on LLM research and tooling, rapidly prototyping new techniques (function calling, structured outputs, reasoning traces, multi-agent collaboration).
  
  

• 3+ years of ML engineering or applied AI experience, with hands-on work deploying LLM-based applications in production.
• Deep expertise in prompt engineering: few-shot learning, chain-of-thought, ReAct patterns, function calling, structured output generation.
• Proficiency in Python ML/AI stack: LangChain, LlamaIndex, Hugging Face, OpenAI/Anthropic APIs, vector DB integrations.
• Experience with agent frameworks: LangGraph, Autogen, or custom orchestration systems for multi-step reasoning and tool use.
• Strong understanding of RAG pipelines: embedding models, semantic search, retrieval strategies, context window optimization.
• NLP and text processing skills: document parsing, entity extraction, summarization, semantic similarity, clustering.
• Hands-on experience with evaluation and testing: building test datasets, metrics for generation quality, human-in-the-loop review processes.
• Familiarity with MLOps principles: model versioning, experiment tracking (MLflow, Weights & Biases), monitoring, drift detection.
• Fluency in English.
• Client-first mentality.
• Intense work ethic.
• Collaborative spirit and problem-solving approach.
  
  

• Experience with structured data + LLM fusion: text-to-SQL, table understanding, chart-to-text generation, KPI narratives.
• Domain knowledge in pharma or commercial analytics: understanding brand metrics, market dynamics, competitive positioning, access barriers.
• Exposure to LLMOps tooling: Langfuse, LiteLLM, guardrails (NeMo, Guardrails AI), cost optimization strategies.
• Fine-tuning and model customization experience: domain adaptation, instruction tuning, RLHF, distillation.
• Consulting mindset: translating ambiguous business needs into technical AI solutions, rapid prototyping, client education on AI capabilities and limitations.
• Experience with multi-agent systems or workflow automation using AI.
  
  

• Cross-functional skills development & custom learning pathways.
• Milestone training programs aligned to career progression opportunities.
• Internal mobility paths that empower growth via s-curves, individual contribution and role expansions
  
  

ZS is committed to a Flexible and Connected way of working. ZSers are onsite at clients or ZS offices three days a week. Combined flexibility to work remotely two days a week is also available. The magic of ZS culture and innovation thrives in both planned and spontaneous face-to-face connections.

Perks & Benefits:

ZS offers a comprehensive total rewards package including health and well-being, financial planning, annual leave, personal growth and professional development. Our robust skills development programs, multiple career progression options and internal mobility paths and collaborative culture empowers you to thrive as an individual and global team member.

We are committed to giving our employees a flexible and connected way of working. A flexible and connected ZS allows us to combine work from home and on-site presence at clients/ZS offices for the majority of our week. The magic of ZS culture and innovation thrives in both planned and spontaneous face-to-face connections.

Travel:

Travel is a requirement at ZS for client facing ZSers; business needs of your project and client are the priority. While some projects may be local, all client-facing ZSers should be prepared to travel as needed. Travel provides opportunities to strengthen client relationships, gain diverse experiences, and enhance professional growth by working in different environments and cultures.

Considering applying?

At ZS, we honor the visible and invisible elements of our identities, personal experiences, and belief systems—the ones that comprise us as individuals, shape who we are, and make us unique. We believe your personal interests, identities, and desire to learn are integral to your success here. We are committed to building a team that reflects a broad variety of backgrounds, perspectives, and experiences. Learn more about our inclusion and belonging efforts and the networks ZS supports to assist our ZSers in cultivating community spaces and obtaining the resources they need to thrive.

If you’re eager to grow, contribute, and bring your unique self to our work, we encourage you to apply.

ZS is an equal opportunity employer and is committed to providing equal employment and advancement opportunities without regard to any class protected by applicable law.

To complete your application:

Candidates must possess or be able to obtain work authorization for their intended country of employment.An on-line application, including a full set of transcripts (official or unofficial), is required to be considered.

NO AGENCY CALLS, PLEASE.

Find Out More At:

www.zs.com