Lead Generative AI Machine Learning Engineer

Aegistech; New York (NY); On-site; USD 140,000 - 350,000; Full time

Job summary

An established industry player is seeking a Lead ML Engineer to drive innovation in machine learning operations. This pivotal role involves architecting and managing the ML model lifecycle, optimizing performance, and collaborating with cross-functional teams to integrate solutions into production. The ideal candidate will have extensive experience in Python, MLOps, and data analytics, contributing to a transformative AI-driven environment. This is an exciting opportunity to be part of a world-class AI/ML team and shape the future of risk management through cutting-edge technology.

Qualifications

• 8+ years of experience in data analytics or machine learning roles.
• 5+ years of production-level Python or Scala coding experience.
• Experience with operationalizing large-scale data pipelines.

Responsibilities

• Architect and manage the ML model development lifecycle.
• Collaborate with teams to integrate ML models into production.
• Optimize models for performance and adapt to new data.

Skills

Python

MLOps

Machine Learning

Data Analytics

Kubernetes

Cloud Platforms

CI/CD

Data Pipelines

Education

Bachelor's Degree in Computer Science

Tools

SQL

No-SQL Databases

Containerization

Workflow Orchestration Tools

Job description

Lead Generative AI Machine Learning Engineer

Direct message the job poster from Aegistech

Lead Technical Recruiter/Manager @ Aegistech Inc.

Our client is seeking a Lead ML Engineer to join our ML team within the Data Science group. As a Lead ML Engineer, you will contribute to the deployment, monitoring, and management of machine learning models and data pipelines. You will work with a peer group of ML engineers to develop ML modules and end-to-end engineering solutions.

In this role, you will play a pivotal role in implementing our machine learning engineering operations, ensuring the seamless deployment, monitoring, and management of our machine learning models and data pipelines.

You will work closely with a world-class AI/ML team comprised of experts in AI/ML modeling, ML engineers, data science, and data engineering teams. You will contribute to engineering and developing solutions for ML operations and be a critical part of leading our client’s AI-driven transformation to drive value internally and for our customers.

Our client is a leader in automation and AI/ML to transform risk management. This role is a unique opportunity for ML/LLMops engineers to grow into the next step in their career journey.

Responsibilities and Impact:

• Architect, develop, and manage the machine learning model development and deployment lifecycle to launch GenAI and ML services end to end.
• Work on large-scale, stateful and stateless distributed systems, including infrastructure, data ingestion platforms, SQL and no-SQL databases, microservices, orchestration services, and more.
• Collaborate with cross-functional teams to integrate machine learning models into production systems.
• Create and manage documentation and knowledge bases, including development best practices, MLOps/LLMOps processes, and procedures.
• Work closely with technology teams in developing and implementing the Enterprise AI platform.
• Fine-tune and optimize models to enhance performance, adapt to new data, or meet specific use case requirements.

Qualifications:

• Bachelor’s degree in computer science, engineering, or a related field.
• 8+ years of experience as a data analytics, machine learning engineer, or similar roles.
• At least 5 years of experience in data science, data analytics, or related fields.
• 5+ years of experience with production-level, scalable Python (or Scala) code.
• Experience with MLOps/LLMOps, machine learning engineering, Big Data, or related roles.
• Knowledge of containerization, Kubernetes, cloud platforms, CI/CD, and workflow orchestration tools.
• 2-3 years of experience operationalizing large-scale data pipelines for batch and stream analytics (Preferred).
• Experience contributing to open-source projects or research initiatives, or participating in Kaggle competitions (Preferred).
• 6-12 months of experience with RAG pipelines, prompt engineering, or Generative AI use cases (Preferred).

After applying, connect directly with the recruiter at https://www.linkedin.com/in/jpandya/

Additional Details

• Seniority level: Mid-Senior level
• Employment type: Full-time
• Job function: Information Technology
• Industries: Investment Banking, Capital Markets, Financial Services

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