Senior AI/ML Engineer

Role Overview

• 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
• 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
• 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
• 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
• 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

Requirements

• 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

• AWS
• Azure
• Cloud
• Python
• PyTorch
• SQL

Benefits

• Competitive pay
• Excellent benefits