Sr. Principal Machine Learning Engineer

Role Overview 

You will work closely with product managers, software engineers, data scientists, architects, and business stakeholders to develop production-ready AI systems leveraging machine learning, deep learning, large language models, retrieval-augmented generation, Small Language Models, intelligent agents, and modern MLOps practices. 

This is a senior individual contributor role requiring deep technical expertise, strong architectural thinking, and the ability to influence engineering practices across teams. The successful candidate will be expected to build scalable solutions, establish engineering best practices, mentor peers, and drive adoption of modern AI and machine learning capabilities. 

This role is ideal for an engineer who enjoys solving complex business problems through innovative AI technologies while maintaining a strong focus on scalability, reliability, governance, and user value. 

Key Responsibilities 

AI/ML Solution Architecture and Engineering 

• Architect and build production-grade machine learning, deep learning, generative AI, and agentic AI systems for commercial and field engagement use cases. 

• Lead technical design for solutions involving retrieval-augmented generation, embeddings, semantic search, LLM orchestration, Text2SQL, recommendation systems, predictive modeling, and intelligent workflow automation. 

• Design scalable AI services and APIs that integrate with enterprise data platforms, business applications, CRM ecosystems, and downstream product workflows. 

• Design, train, fine-tune, and deploy neural network models for domain-specific commercial and field use cases, including deep learning architectures such as Transformers, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and LSTMs as applicable. 

• Develop deep learning solutions that prioritize performance, generalization, explainability, scalability, and production readiness. 

• Apply frameworks such as PyTorch and TensorFlow to build, experiment with, and productionize neural network models. 

• Build modular, reusable, observable, secure, and maintainable solutions aligned with enterprise technology patterns. 

End-to-End Model and GenAI Delivery 

• Own the full lifecycle of AI/ML delivery, including problem framing, data preparation, feature engineering, experimentation, model training, prompt and context design, evaluation, deployment, monitoring, and continuous improvement. 

• Develop and productionize NLP and LLM capabilities, including RAG, prompt engineering, model adaptation, fine-tuning where appropriate, and response quality evaluation. 

• Train and fine-tune neural network and language models using full fine-tuning, instruction tuning, domain adaptation, and parameter-efficient techniques such as LoRA and QLoRA to optimize performance for specific business tasks and datasets. 

• Apply model distillation techniques to compress large teacher models into smaller, efficient student models, balancing accuracy, latency, infrastructure cost, and operational constraints for production deployment. 

• Design, train, evaluate, and deploy Small Language Models (SLMs) as lightweight, cost-efficient alternatives to large language models for latency-sensitive, on-device, resource-constrained, or specialized domain use cases, including task-specific SLM fine-tuning and evaluation against larger models. 

• Evaluate trade-offs between foundation models, fine-tuned models, distilled models, and Small Language Models to determine the most effective architecture for business, operational, and governance requirements. 

• Implement model evaluation frameworks for predictive, generative, and retrieval-based systems, including accuracy, relevance, groundedness, hallucination risk, latency, cost, and robustness. 

• Monitor production performance, data drift, model drift, failures, and usage patterns, and drive remediation or optimization. 

Data, Platform, and MLOps Engineering 

• Build and operate scalable data and feature pipelines using cloud-native and enterprise data platforms. 

• Implement MLOps and LLMOps practices including CI/CD, model registry, experiment tracking, version control, reproducibility, automated testing, observability, lineage, and auditability. 

• Work with platforms such as Databricks, SageMaker, Azure or AWS services, Kubernetes, Docker, MLflow, or similar tools based on approved enterprise patterns. 

• Partner with data engineering and platform teams to ensure data quality, governance, lineage, access control, and operational reliability. 

Business Partnership and Product Impact 

• Work closely with product owners, business stakeholders, data scientists, architects, compliance, and engineering teams to translate business opportunities into AI/ML product capabilities. 

• Shape technical approaches for high-value use cases across HCP targeting, field engagement, content recommendation, analytics, workflow automation, and decision intelligence. 

• Communicate model behavior, design tradeoffs, risks, and recommendations clearly to technical and non-technical audiences. 

• Influence product roadmap decisions by bringing practical AI/ML feasibility, scalability, governance, and value considerations into planning. 

Responsible AI, Security, and Governance 

• Apply secure-by-design, privacy-by-design, and responsible AI principles across the AI/ML lifecycle. 

• Ensure AI systems are designed with appropriate controls for explainability, traceability, bias awareness, grounding, auditability, and human oversight where needed. 

• Collaborate with governance, compliance, quality, and risk partners to ensure solutions meet standards for data use, reliability, documentation, and operational readiness. 

• Create and maintain architecture, design, evaluation, and support documentation for production AI systems. 

Technical Leadership and Coaching 

• Act as a senior technical contributor and guide for AI/ML engineers, data scientists, and product teams. 

• Promote reusable patterns, engineering standards, best practices, and evaluation approaches across multiple teams. 

• Review solution designs, code, architecture choices, and operational patterns to improve quality and consistency. 

• Support team capability building through mentoring, technical coaching, knowledge sharing, and hands-on examples. 

Required Qualifications 

• 6+ years of experience building and deploying production machine learning or AI solutions. 

• 3+ years working with generative AI technologies, including LLMs, RAG architectures, embeddings, prompt engineering, model evaluation, and agentic workflows. 

• Strong proficiency in Python and SQL. 

• Hands-on experience building and fine-tuning deep learning models using PyTorch; TensorFlow experience preferred. 

• Experience with modern AI/ML frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, LangChain, DSPy, or equivalent technologies. 

• Experience designing and deploying AI services on AWS, Azure, or GCP. 

• Experience implementing MLOps or LLMOps practices including CI/CD, monitoring, observability, experiment tracking, model management, and automated testing. 

• Strong software engineering fundamentals including API development, system design, version control, code reviews, and testing. 

• Strong understanding of neural network architectures, transfer learning, model optimization, model evaluation, and production deployment considerations. 

• Experience working with structured, semi-structured, and unstructured data at scale. 

• Ability to communicate complex technical concepts to technical and non-technical audiences. 

• Demonstrated ability to translate ambiguous business challenges into scalable technical solutions that deliver measurable business value. 

• Demonstrated experience leading technical initiatives and influencing architectural decisions across teams. 

Preferred Qualifications 

• Experience with commercial pharma, healthcare analytics, CRM, customer engagement, decision-support platforms, HCP data, field engagement workflows, targeting/orchestration, or Veeva-related ecosystems. 

• Experience with enterprise AI/data platforms such as Databricks, SageMaker, Azure ML, or similar. 

• Experience with GenAI patterns such as multi-agent systems, tool use, Text2SQL, semantic search, knowledge graphs, hybrid retrieval, reranking, context engineering, and LLM evaluation. 

• Experience with foundation model fine-tuning, instruction tuning, LoRA, QLoRA, model compression, model distillation, quantization, and Small Language Model development. 

• Experience evaluating and optimizing AI systems across quality, latency, throughput, scalability, and cost dimensions. 

• Experience with FastAPI, Flask, or similar frameworks for model/API serving. 

• Experience with Kubernetes, Docker, GitHub Actions, MLflow, vector databases, Spark, PySpark, or lakehouse architectures. 

• Familiarity with responsible AI practices such as explainability, bias detection, grounded generation, guardrails, auditability, and human-in-the-loop design. 

• Experience creating executive-facing dashboards, model insights, or AI observability views using tools such as Power BI, Plotly, Dash, or similar. 

• Strong documentation, design-review, and technical governance habits. 

• M.Tech, MS, or higher degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related quantitative discipline preferred. 

Key Skills 

• • Machine Learning Engineering 

  • Small Language Models (SLMs) 

  • Deep Learning and Neural Network Architecture Design 

  • Model Evaluation and Error Analysis 

  • Generative AI and LLM Engineering 

  • Python, SQL, PySpark 

  • Retrieval-Augmented Generation 

  • Databricks, SageMaker, AWS/Azure 

  • Agentic AI and Multi-Agent Workflows 

  • MLOps / LLMOps 

  • Prompt Engineering and Context Engineering 

  • CI/CD, Docker, Kubernetes, Git 

  • PyTorch and TensorFlow 

  • Model Monitoring and Drift Detection 

  • Transformer Models, CNNs, RNNs, and LSTMs 

  • API Development and AI Service Integration 

  • Model Fine-Tuning, Instruction Tuning, and Domain Adaptation 

  • Responsible AI, Security, Privacy, and Governance 

  • LoRA, QLoRA, and Parameter-Efficient Fine-Tuning 

  • Business-facing Technical Leadership 

  • Model Distillation, Compression, and Quantization