Senior AI Engineer / Senior Data Scientist - (Code: SAI-104)

Company Description:

Exandia Corp. (www.exandia.com) specializes in developing innovative integrated information systems and modern digital services for customers worldwide. Exandia Corp. leads large-scale projects across multiple industries, including education, fintech, healthcare, logistics, and the public sector. With a commitment to innovation and excellence, Exandia Corp. stands as a trusted partner in transforming industries through technology.

Position Summary:

We are seeking a Senior AI Engineer / Senior Data Scientist to lead the design, development, evaluation, and deployment of advanced AI and machine learning solutions at our customer’s software platform. The ideal candidate will have strong hands-on experience with machine learning, ensemble learning, deep learning, time-series analysis, predictive modeling, and modern LLM/generative AI workflows. This person should be able to work independently, define technical approaches, guide junior team members, and translate ambiguous business or engineering problems into practical AI solutions. This role requires both technical excellence and strong personal qualities: clear communication, humility, willingness to listen, ability to collaborate, and the discipline to align technical work with company goals rather than pursuing complexity for its own sake.

What You Will Do:

AI/ML Model Development

-  Design, build, evaluate, and improve machine learning models for real-world operational and engineering data.

- Develop predictive models that identify patterns, anomalies, trends, and actionable insights from large datasets.

- Apply classical ML techniques including regression, classification, clustering, feature engineering, dimensionality reduction, and model selection.

- Build and tune ensemble learning methods such as Random Forests, Gradient Boosting, XGBoost, LightGBM, CatBoost, stacking, and blending.

- Develop robust model evaluation frameworks using appropriate metrics, validation strategies, cross-validation, backtesting, and error analysis.

- Ensure models are interpretable, reliable, maintainable, and aligned with business and engineering needs.

Deep Learning & Advanced Analytics

-  Design and implement deep learning models where appropriate, including neural networks for time-series, signal, sensor, or telemetry data.

-   Work with frameworks such as PyTorch, TensorFlow, Keras, or similar tools.

-  Apply advanced approaches for sequence modeling, anomaly detection, forecasting, representation learning, and pattern recognition.

-   Evaluate when deep learning is appropriate and when simpler models are better.

-  Balance model performance with explainability, maintainability, latency, cost, and operational constraints.

LLMs & Generative AI

- Explore, prototype, and deploy LLM-powered capabilities to improve internal workflows, customer-facing products, analytics, documentation, and decision support.

- Work with OpenAI APIs or similar LLM platforms to build useful AI assistants, summarization tools, retrieval-augmented generation workflows, and natural-language interfaces.

- Understand prompt engineering, embeddings, vector databases, RAG pipelines, evaluation, guardrails, and model limitations.

-  Identify practical use cases for generative AI without overpromising or misapplying the technology.

-  Help establish standards for safe, reliable, and measurable use of LLMs within the company.

Data Science, Data Pipelines & Experimentation

- Work with large, complex datasets including time-series, telemetry, fi eld, engineering, and operational data.

- Collaborate with software/backend teams to design data pipelines that support AI model development and deployment.

- Use Python, pandas, NumPy, SQL, scikit-learn, and cloud-based data tools to analyze and transform data.

- Defi ne clear experiments, document assumptions, track results, and communicate conclusions.

- Improve data quality, feature stores, experiment reproducibility, and analytical workflows.

- Work with stakeholders to understand the operational meaning of the data and validate AI outputs against real-world expectations.

Productionization & Technical Leadership

- Help move models from prototype to production in collaboration with backend, DevOps, and product teams.

-  Design AI systems that are scalable, monitored, testable, secure, and maintainable.

-  Define model inputs, outputs, APIs, performance expectations, and monitoring requirements.

- Collaborate on deployment workflows, CI/CD, model versioning, logging, alerting, and retraining strategies.

- Provide technical guidance, code reviews, mentorship, and best practices for junior AI/data team members.

-  Break down ambiguous AI initiatives into clear technical plans, milestones, and deliverables.

Cross-Functional Collaboration

- Work closely with leadership, product, engineering, domain experts, and business stakeholders to identify high-value AI opportunities.

-  Communicate complex AI concepts in clear, practical language.

-  Help prioritize AI work based on impact, feasibility, risk, and business value.

-  Listen carefully to feedback from domain experts and customers.

-  Support a culture of continuous learning, intellectual honesty, and practical execution.

Required Qualifications

- Bachelor’s or graduate degree in Computer Science, Data Science, Artifi cial Intelligence, Machine Learning, Statistics, Engineering, Physics, Mathematics, or a related technical fi eld.

- 3-5 years of professional experience in data science, machine learning, AI engineering, or applied analytics.

-  Strong hands-on Python experience.

-  Strong experience with pandas, NumPy, scikit-learn, SQL, and data analysis workfl ows.

-  Proven experience developing, evaluating, and improving machine learning models.

- Experience with ensemble learning methods such as Random Forests, Gradient Boosting, XGBoost, LightGBM, CatBoost, or similar.

-  Experience with deep learning frameworks such as PyTorch, TensorFlow, or Keras.

-  Familiarity with LLMs, generative AI, embeddings, prompt engineering, RAG, or AI assistant development.

- Strong understanding of model evaluation, feature engineering, overfi tting, validation strategies, and production considerations.

- Ability to work with messy, real-world datasets and turn them into usable analytical or modeling pipelines.

-  Strong written and verbal English communication skills.

-  Ability to explain technical work clearly to both technical and non-technical audiences.

-  Ability to independently defi ne technical direction and drive projects to completion.

Preferred Qualifications

- Experience with time-series modeling, signal processing, sensor data, telemetry, IoT data, or real-time analytics.

-  Experience deploying ML models into production systems.

- Familiarity with AWS services such as S3, Lambda, EC2, RDS/Aurora, Kinesis, SQS, IoT Core, or SageMaker.

-  Experience with FastAPI, REST APIs, Docker, Kubernetes, CI/CD, or cloud-native development.

- Experience with vector databases, LangChain, LlamaIndex, OpenAI APIs, or similar LLM application frameworks.

-  Experience with MLOps tools such as MLfl ow, Weights & Biases, DVC, Airfl ow, Prefect, or similar.

- Prior experience in oil and gas, energy technology, industrial analytics, geophysics, physics-based modeling, or operational optimization.

-  Experience mentoring junior data scientists, analysts, or engineers.

-  Ability to combine physics/domain understanding with data-driven modeling.

What We’re Looking For

- We are looking for a senior technical contributor who is strong, practical, humble, and highly collaborative. The successful candidate will be:

-   Technically excellent but humble: Strong enough to lead but mature enough to listen.

-   Practical: Focused on solving real business and engineering problems, not just using fashionable tools.

-   Clear communicator: Able to explain assumptions, tradeoffs, results, and limitations.

-   Collaborative: Works well with leadership, engineering, product, and domain experts.

-   Coach and mentor: Helps junior team members grow while maintaining high standards.

-   Curious and continuously learning: Stays current with AI, ML, deep learning, and LLM developments.

-   Disciplined: Documents work, validates results, and avoids unsupported claims.

-   Ownership-minded: Takes responsibility for outcomes, not just code or experiments.

-  Open to feedback: Willing to adjust approach based on evidence, leadership direction, and business priorities.

-   Culturally aligned: Respectful, professional, reliable, and easy to work with.

Why Join Us:

·   Work on cutting-edge AI-powered technology.

·   Remote-first culture with flexible working arrangements.

·   Opportunity to impact healthcare through technology.

·   Collaborative team of experienced scientists and engineers.

·   Competitive compensation package.