Cloud AI/ML Engineer

Innovation Hub Overview 

Jefferies is creating a Technology Innovation Hub in Pune, a greenfield opportunity to build the systems that power global markets. As our first India technology center, this hub brings together hands on builders who engineer the platforms behind Jefferies’ growth across capital markets, investment banking, and institutional securities. We’re scaling toward an elite team of 500 engineers while maintaining the agility, ownership, and meritocratic spirit that defines Jefferies. From cloud and data to AI, risk, and core business technologies, teams in Pune will lead high impact work with a global mandate.

IT Infrastructure Technology 

Jefferies’ IT Infrastructure Technology team builds and runs the core technology backbone that enables the firm to operate globally. It covers enterprise infrastructure engineering and operations while driving modernization initiatives such as Cloud Adoption and Network Resiliency. The group supports critical platforms including networking, end-user computing, cloud, databases, server/Unix engineering, communications, and global data centers (including low-latency colocations near exchanges). 

Key functions: 

• Networking / Telecom / Multimedia Engineering
• Windows / Desktop / Messaging Engineering.  
• Private / Public Cloud / Database / Unix Engineering.
• Data Centers. 

 Role summary

Build and operate production machine learning infrastructure and pipelines on AWS to enable scalable, reliable ML model deployment and serving. As an ML Engineer, you will design ML platforms, implement MLOps workflows, deploy models to production, and ensure monitoring and governance. You will partner with Data Scientists, Data Engineers, and business stakeholders to operationalize machine learning and deliver AI-powered capabilities that drive business value.
You will collaborate closely with Data Scientists, Data Engineers, Data Platform Engineers, Cloud Architecture, Cloud Platform, Cloud Security, and business stakeholders across Equities, Investment Banking, and other divisions. Strong teamwork, customer service orientation, and the ability to translate research into production systems are essential. Experience working in Agile teams using Jira and Confluence is expected

 Key responsibilities

• Design, build, and operate ML infrastructure and platforms on AWS using services such as SageMaker, EKS, Lambda, Step Functions, ECR, and S3
• Implement end-to-end MLOps pipelines: model training, evaluation, versioning, deployment, monitoring, and retraining; ensure automation and reproducibility
• Deploy and serve ML models in production using SageMaker endpoints, containerized microservices (EKS/ECS), or serverless architectures (Lambda); ensure scalability, reliability, and low latency
• Build Infrastructure as Code (Terraform) for ML infrastructure; follow team standards for modules, state management, and Terraform Enterprise workflows
• Implement CI/CD pipelines for ML workflows using GitHub, Bamboo, GitLab, and tools like MLflow, Kubeflow, or SageMaker Pipelines
• Establish ML model governance and security controls: model registry, version control, access controls, audit logging, and compliance with regulatory requirements
• Monitor model performance and data drift; implement alerting and automated retraining workflows to maintain model accuracy
• Collaborate with Data Scientists to translate research models into production-ready systems; optimize models for inference performance and cost
• Partner with Data Engineers to build feature engineering pipelines and ensure data quality for ML workflows
• Work with Platform Engineering, and Cloud Security teams to ensure ML infrastructure aligns with enterprise standards and best practices 
• Troubleshoot complex issues spanning ML models, infrastructure, Kubernetes, AWS services, and data pipelines
• Document ML systems, operational procedures, and best practices; contribute to team knowledge base
• Drive adoption of GenAI and agentic AI workflows; implement AI agents safely in enterprise environments
• Proficiency using GenAI assistants (ChatGPT, Claude, GitHub Copilot) for ML code development, pipeline design, troubleshooting, and documentation
• Demonstrated ability to implement AI agent-driven automation (agentic workflows) safely in enterprise ML environments: establish human-in-the-loop controls for model deployment, maintain comprehensive logging and auditability, implement guardrails to prevent unintended model behavior, design rollback mechanisms, and ensure secure credential and data handling
• Strategic mindset to identify ML engineering toil; ship automation that measurably reduces manual work in model deployment, monitoring, and retraining workflows
• Monitor ML model performance and infrastructure health; respond to incidents and degradations promptly
• Participate in on-call rotation as needed for production ML systems
• Conduct post-incident reviews for ML system failures; track corrective actions to completion
• Maintain runbooks and operational documentation for ML infrastructure and pipelines
• Continuously improve ML system reliability, performance, cost efficiency, and model accuracy

 Requirements

 7+ years in ML engineering, MLOps, data engineering, or software engineering roles with 3+ years focused on production ML systems

• Strong experience with AWS ML/AI services: SageMaker, Bedrock, Lambda, EKS, ECR, S3, and Step Functions
• Proficiency in Python and ML frameworks: TensorFlow, PyTorch, scikit-learn, XGBoost, or similar
• Experience building and deploying ML models at scale: training pipelines, model serving, inference optimization
• Strong understanding of MLOps practices: CI/CD for ML, model versioning, monitoring, drift detection, and retraining
• Proficiency with containerization (Docker) and Kubernetes (EKS) for ML workloads
• Experience with Infrastructure as Code (Terraform) and CI/CD pipelines 
• Solid understanding of data engineering: ETL/ELT, feature engineering, data quality, and data pipelines
• Strong software engineering skills: version control (Git), testing, code review, and production-grade coding practices
• Excellent problem-solving and troubleshooting skills
• Strong communication and collaboration skills; ability to work with Data Scientists, Engineers, and business stakeholders

Preferred

• AWS Certified Machine Learning – Specialty certification
• Experience with Databricks or Snowflake for ML workflows
• Familiarity with MLflow, Kubeflow, or similar ML platforms
• Background in data science or statistical modeling
• Experience with GenAI models (LLMs, embeddings, RAG architectures) and AWS Bedrock
• Knowledge of model explainability, fairness, and responsible AI practices
• Experience with streaming ML and real-time inference

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