Dexian
Job Title: AI & Machine Learning Engineer
Location:
Tampa, FL (onsite)
Type:
Full-Time
About the Role
We are seeking a
mid-level AI & Machine Learning Engineer
to play a key role in establishing and scaling our ML/AI capabilities from the ground up. This is a hands-on role where you'll design, build, and deploy solutions that directly impact core business operations. You'll collaborate with cross-functional stakeholders to identify high-value use cases, prototype models, and deliver production-ready AI applications that improve efficiency, optimize decision-making, and create measurable business outcomes.
This is a rare opportunity to join at the
early stage of AI adoption
, where you'll help shape the strategy, lay the technical foundation, and scale ML/AI initiatives across the organization.
Key Responsibilities
Partner with business teams to identify and prioritize ML/AI use cases with the highest value impact.
Design, build, and deploy predictive models and ML pipelines from scratch.
Develop prototypes and scale them into production-ready applications.
Package models for consumption through APIs, dashboards, or internal tools.
Continuously monitor, retrain, and optimize models to ensure business relevance.
Collaborate with stakeholders to translate real-world challenges into technical ML solutions.
Establish best practices for MLOps, model governance, and AI adoption across teams.
Ideal Candidate Profile
3–5 years of experience as a
Machine Learning Engineer, Data Scientist, or similar role
.
Strong skills in
Python
and
SQL
, with experience in frameworks such as
Scikit-learn, TensorFlow, PyTorch, or XGBoost
.
Proven ability to take models from
development to production
(batch or real-time).
Solid understanding of model evaluation, performance metrics, and aligning models with business objectives.
Experience building solutions in
cloud environments
(GCP, AWS, or Azure) and using
MLOps tools
(e.g., MLflow, Vertex AI, SageMaker).
Comfortable working directly with non-technical stakeholders to define requirements and demonstrate impact.
Nice-to-Haves
Background in sales, operations, logistics, or customer service use cases.
Familiarity with NLP techniques, call transcript analysis, or conversational AI.
Experience with route optimization, capacity planning, or workforce scheduling.
Exposure to
data visualization tools
(Tableau, Power BI, or similar).
Success in this Role Will Be Measured By
Adoption of ML/AI models across business teams.
Tangible improvements in efficiency, forecasting, or decision-making.
Ability to design scalable, reusable ML pipelines.
Measurable business impact driven by deployed AI solutions.