Overview

Senior AI/ML Engineers are senior individual contributors who design, build, and deploy production-grade AI/ML systems for both client-facing and internal products. They partner with leadership and cross-functional teams to translate business needs into scalable ML and LLM-based solutions.

This role does not typically include direct reports but requires strong technical leadership, mentorship, and influence across teams.

This role is open to US based applicants only.

Responsibilities

AI/ML System Design & Leadership

• Lead the design and implementation of scalable ML systems, including supervised, unsupervised, and LLM-based solutions
• Translate research and prototypes into production-ready systems
• Partner with stakeholders to identify high-impact AI/ML opportunities and define optimal technical approaches
• Provide technical mentorship and contribute to team upskilling

LLM & Production AI Systems

• Build and operate LLM pipelines, including prompt design, fine-tuning, and evaluation
• Develop RAG-based systems using embeddings, vector stores, and retrieval strategies
• Design evaluation frameworks, feedback loops, and datasets to continuously improve model performance
• Create reusable tooling to accelerate experimentation, deployment, and monitoring

MLOps & Deployment

• Own end-to-end ML lifecycle: data pipelines, training, deployment, monitoring, and iteration
• Establish best practices for reproducibility, observability, CI/CD, and model versioning
• Partner with platform/DevOps teams to ensure reliability and scalability
• Promote responsible AI practices, including governance, fairness, and transparency

Cross-Functional Collaboration

• Lead cross-functional initiatives across data engineering, analytics, and AI/ML
• Translate complex ML concepts into clear recommendations for technical and non-technical audiences
• Collaborate with clients and internal teams to plan and deliver AI/ML solutions
• Contribute documentation, frameworks, and shared best practices

Project Execution

• Scope and lead complex AI/ML initiatives aligned to business outcomes
• Align stakeholders and drive execution across teams
• Establish clear success metrics and ensure delivery of high-impact solutions

Skills & Qualifications

• 5–7 years of experience in ML engineering, AI engineering, or related fields, with production deployment experience
• Strong programming skills in Python and SQL; experience with PyTorch and HuggingFace
• Experience building LLM applications, including RAG, embeddings, and vector search
• Experience with cloud platforms (AWS or Azure; e.g., SageMaker, Bedrock, Azure ML)
• Strong understanding of ML fundamentals: data design, training, evaluation, and experimentation
• Familiarity with LLM alignment techniques (e.g., SFT, DPO, RL)
• Experience with MLOps practices: CI/CD, monitoring, retraining, and experiment tracking
• Proficiency working with complex, multi-source datasets and defining evaluation strategies
• Strong software engineering fundamentals (testing, modularity, code review)
• Experience mentoring engineers and influencing technical direction
• Strong communication skills with both technical and non-technical stakeholders

Tech Stack

• Languages: Python, SQL
• Frameworks: PyTorch, HuggingFace
• Platforms: AWS, Azure, Snowflake, Databricks

Physical Requirements

• Frequent sitting at a desk performing work on a computer
• Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions

Compensation Range: $175,000 - 190,000 annually

Please note that compensation packages are finalized after the interview process is concluded. We use a competency-based approach to base pay, which means it is based on the competencies and skills demonstrated for this role.