Job Responsibilities

• Combine vast data assets with advanced AI, including LLMs and Multimodal LLMs
• Bridge scientific research and software engineering, applying expertise in both domains
• Collaborate with engineering teams to lead the design and delivery of GenAI products
• Architect and implement scalable AI Agents, Agentic Workflows, and GenAI applications
• Integrate GenAI solutions with enterprise platforms using API-based methods
• Establish validation procedures with Evaluation Frameworks, bias mitigation, safety protocols, and guardrails
• Align ML problem definition with business objectives
• Communicate technical information and ideas effectively to stakeholders

Required Qualifications, Capabilities, and Skills

• Bachelor's Degree in a quantitative discipline such as Computer Science, Mathematics, or Statistics
• Ten years of experience in an individual contributor role in ML engineering
• Strong understanding of statistics, optimization, and ML theory, focusing on NLP and/or Computer Vision algorithms
• Demonstrated experience in parameter-efficient fine-tuning, model quantization, and quantization-aware fine-tuning of LLM models
• Experience integrating GenAI solutions with enterprise platforms via standardized API patterns
• Ability to establish validation procedures, including Evaluation Frameworks, bias mitigation, safety protocols, and guardrails
• Excellent grasp of computer science fundamentals and SDLC best practices
• Strong communication skills to build trust with stakeholders

Preferred Qualifications, Capabilities, and Skills

• PhD in a quantitative discipline such as Computer Science, Mathematics, or Statistics

• Experience designing and implementing pipelines using DAGs such as Kubeflow, DVC, or Ray
• Ability to construct batch and streaming microservices exposed as gRPC or GraphQL endpoints
• Hands-on experience implementing distributed, multi-threaded, and scalable applications using frameworks such as Ray, Horovod, or DeepSpeed