Machine Learning Engineer Quantization Jobs in Auburndale, FL

ATTENTION MILITARY AFFILIATED JOB SEEKERS - Our organization works with partner companies to source qualified talent for their open roles. The following position is available to Veterans, Transitioning Military, National Guard and Reserve Members, Military Spouses, Wounded Warriors, and their Caregivers. If you have the required skill set, education requirements, and experience, please click the submit button and follow the next steps. Unless specifically stated otherwise, this role is "On-Site" at the location detailed in the job post.
What you will do
Let's do this. Let's change the world. In this vital role you will play a pivotal role in building and scaling our machine learning models from development to production. Your expertise in both machine learning and operations will be essential in creating efficient and reliable ML pipelines. A background in data engineering, including experience with data pipelines and distributed data processing, is a strong plus.
Roles & Responsibilities:
Lead the end-to-end design, development, and delivery of machine learning and Generative AI (GenAI) solutions, from problem framing to production deployment and business impact realization.
Act as the technical owner for large-scale ML/GenAI initiatives, driving architecture decisions, scalability, reliability, and long-term maintainability.
Design and implement advanced agentic AI systems, including multi-agent architectures, reasoning workflows, tool integration, and autonomous decision-making systems.
Define and institutionalize evaluation, validation, and governance frameworks for ML/GenAI systems, including model performance, prompt evaluation, safety guardrails, hallucination mitigation, and compliance.
Partner directly with business stakeholders and product leaders to understand objectives, translate them into AI/ML solutions, and ensure measurable value delivery.
Establish and enforce best practices in MLOps, LLMOps, and DevOps, including CI/CD, monitoring, observability, reproducibility, and cost optimization.
Architect and oversee scalable cloud-based ML/GenAI platforms leveraging AWS, GCP, or Azure.
Drive experimentation strategy, including A/B testing, prompt optimization, and iterative improvement of models and agent workflows.
Provide technical leadership and mentorship to L4 and L5 engineers, including design reviews, code reviews, and career guidance.
Lead cross-functional collaboration across data science, engineering, product, and business teams to deliver integrated AI solutions.
Stay at the forefront of advancements in machine learning, Generative AI, and agentic systems, and drive adoption of new technologies and approaches.
Design, develop, and implement robust data architectures and platforms to support ML Operation.
Ensuring data integrity, accuracy, and consistency through rigorous quality checks and monitoring.
Additional Qualifications/Responsibilities
What we expect of you
We are all different, yet we all use our unique contributions to serve patients. The professional we seek is a Software Engineer with these qualifications.
Doctorate degree and 2 years of experience
OR
Master's degree and 4 years of experience
OR
Bachelor's degree and 6 years of experience
OR
Associate's degree and 10 years of experience
OR
High school diploma / GED and 12 years of experience
Deep expertise in machine learning, deep learning, and Generative AI (LLMs, transformers, embeddings, fine-tuning techniques).
Proven track record of leading and delivering production-grade ML/GenAI systems end-to-end with measurable business impact with strong experience in designing scalable system architectures for ML and GenAI, including distributed systems and high-throughput pipelines.
Expertise in MLOps/LLMOps ecosystems (MLflow, Kubeflow, Airflow, CI/CD, Docker, Kubernetes).
Strong system design, architecture, and problem-solving skills with the ability to operate independently and lead large initiatives.
Demonstrated proficiency in leveraging cloud platforms (AWS, Azure, GCP) for data engineering solutions. Strong understanding of cloud architecture principles and cost optimization strategies.
Proven ability to mentor and guide junior and mid-level engineers (L4/L5).
Good-to-Have Skills:
Degree in computer science, Statistics, and Data Science preferred. Master's degree and 6+ years experience Or Bachelor's degree and 8+ years' experience
Cloud Computing certificate preferred
Experience with big data ecosystems (Spark, Hadoop) and large-scale data processing.
Strong background in data engineering and building scalable data platforms.
Advanced proficiency in Python and modern ML/AI frameworks (PyTorch, TensorFlow, Hugging Face, LangChain or similar).
Experience designing robust evaluation and validation systems, including automated evals, human-in-the-loop, safety testing, and monitoring frameworks.
Extensive experience with RAG architectures, vector databases, and knowledge-grounded systems.
Strong understanding of agentic AI frameworks, including orchestration, planning, memory, and tool use.