Principal Machine Learning Engineer

About the Role:

We are seeking an exceptional Principal Machine Learning Engineer to join our organization at the forefront of applied AI. This is a senior individual contributor role designed for a practitioner who is equally at home architecting large-scale LLM infrastructure, building scalable python backend APIs, and driving organization wide AI transformation. You will be designing and delivering Generative AI and Agentic AI systems, setting engineering standards for building production grade ML applications, and mentor engineering teams across the organization. You will play a critical role in leading S&P’s AI-driven transformation to drive value internally and for our customers.

The Team:

You will work closely as part of a world-class AI & ML teamcomprisedof Data Science, Machine Learning,MLOpsand Data Engineers. The Agentic AI Platform & ML Engineering team is the central nervous system of our AI capabilities - a high-impact group working at the intersection ofcutting-edgeresearch and real-world product delivery. Weoperatewith a strong engineering culture that moves fast without sacrificing quality, treats AI systems with mission-critical rigor, and obsesses over operationalization because models thataren'tin productionaren'tdelivering value.

Responsibilities and Impact:

• LLM & Generative AI Engineering:Deploy and architect production-scale LLM systemsspanning frontier models (GPT-4 class), open-source variants (such asLLaMA, Mistral, Gemma),RAG pipelines, and multi-modal AI systems incorporating text, code, images, and structured data.

• Agentic AI Systems:Design and operationalize autonomous AI agents with multi-agent orchestration, tool-use capabilities, memory management, and enterprise-grade guardrails and observability strategies.

• Python & Software Engineering:Write high-performance Python code following SOLID principles, lead code reviews, build reusable AI libraries, and implement rigorous testing and CI/CD practices across all ML workloads

• Cloud & Distributed Systems:Architect cloud-native AI infrastructure with GPU cluster management, auto-scaling inference endpoints, vector databases, and cost-optimized distributed systems for high-throughput model serving,leveragingmanaged AI services (such asBedrock, Azure OpenAI, Vertex AI) alongside self-hosted deployments (such asvLLM, TGI).

• Backend APIs & Systems Integration:Design production-gradeRESTful and asynchronous APIs (similar toFastAPI, gRPC)exposing AI capabilities, integrate LLM services with enterprise systems, and own end-to-end performance, reliability, and security from design through production

• MLOps&LLMOps:Implement comprehensive ML pipelines for training through monitoringtools(similar toMLflow, Kubeflow, SageMaker),establishprompt versioning and model governance practices, and instrument production systems with observability across performance and quality metrics

• DevOps & Platform Engineering:Embed AI workloads into CI/CD pipelines, champion containerization(such asDocker, Helm)andGitOpsworkflows, define SRE practices for ML systems, and drive platform standardization for self-service AI capabilities

• Organization-Wide AI Transformation:Adviseengineering, productandbusinessleadership on AI strategy and build-vs-buy decisions, evaluate third-party tooling, define transformation KPIs, and partner with governance teams toestablishresponsible AI policies and regulatory frameworks.

Basic Required Qualifications:

• 10+ years of progressive experience, with 8+ years in data science, data analytics, machine learning engineering, or similar roles. 

• Proven ability to translate complex technical concepts for non-technical audiences with clarity and impact. 

• Experience defining technical roadmaps, architecture decision records (ADRs), and engineering standards adopted across multiple teams. 

• History of mentoring senior and mid-level engineers, conducting effective technical interviews, and raising the organizational engineering bar. 

• LLM Frameworks:Extensive knowledge and experienceintoolssimilar toLangChain, LlamaIndex, LangGraph, Hugging Face Transformers, PEFT, vLLM, Ollama, or equivalent production-grade tooling. 

• MLOps Tooling:Extensive knowledge and experience in toolssimilar toMLflow, SageMaker, Vertex AI, or Kubeflow — with a bias toward automation and repeatability. 

• Cloud Platforms: Deep expertise incloud platforms such as AWS, GCP, or Azure. 

• Python: Expert-level proficiency including async programming, performance optimization, type systems, packaging, and internal library authorship. 

• Databases & Storage: Vector databases (similar toPinecone, OpenSearch, Chroma), relational (such asPostgreSQL), NoSQL (such asRedis, DynamoDB), and object storage. 

• Containerization & Orchestrationexpertisein environmentssimilar toDocker, Helm.

• Backend Development:Expertisein engineering in environmentssimilar toFastAPI, REST design principles, async patterns, OAuth2/JWT, and API security best practices. 

• Distributed Systems:Experience with message queues (similar to SQS), event streaming, microservices design patterns.

Preferred Qualifications:

• MS in Computer Science, Machine Learning, Engineering, or a related quantitative field. 

• Published open-source contributions in theenvironments such asLLM, GenAI, or NLP space. 

• Experience operating in regulated industries (finance, healthcare, legal) with AI compliance, auditability, and risk management requirements. 

• Contributions to enterprise AI governance frameworks, model risk management programs, or responsible AI practices development. 

• Cloud AI certifications: AWS ML Specialty, GCP Professional ML Engineer, Azure AI Engineer Associate, or equivalent. 

Grade – 12 {10 to 14 years of experience}

Shift Time – 12pm to 9pm IST

Working Model – Hybrid, twice a week work from office

Location – Hyderabad