About the Role
We are seeking an experienced Senior AI Engineer for our client to design, build, deploy, and scale AI-powered products and services that create measurable business value. This role focuses on delivery and integration of machine learning and generative AI solutions into enterprise workflows, with strong emphasis on production-grade engineering, MLOps, responsible AI, and cross-functional collaboration. The ideal candidate combines deep Python engineering capability with hands-on experience integrating foundation model APIs, building LLM applications, monitoring model performance in production, and partnering with data scientists, product managers, and platform teams to move from prototype to scalable deployment.
Responsibilities
- Design, develop, deploy, and maintain AI/ML and GenAI solutions using Python for enterprise and business use cases.
- Build and integrate LLM-powered applications using frameworks such as LangChain or LlamaIndex and external model APIs such as OpenAI, Anthropic, Cohere, or Mistral.
- Productionize models and AI services using Flask, FastAPI, MLflow, Triton Inference Server, or Hugging Face Inference Endpoints.
- Collaborate with data scientists to operationalize machine learning models, fine-tuned models, NLP pipelines, and computer vision solutions.
- Develop robust API integrations, orchestration layers, evaluation pipelines, and logging/monitoring capabilities for AI services.
- Implement model versioning, experiment tracking, monitoring, and drift/performance detection in production environments.
- Support data preparation and curation activities, including cleaning, transformation, and readiness of datasets for machine learning tasks.
- Apply best practices for responsible AI, including bias detection and mitigation, explainability, transparency, content filtering, and moderation.
- Work closely with product, DevOps, platform, security, and compliance stakeholders to deliver secure, scalable, and compliant solutions.
- Rapidly prototype MVPs and accelerate experimentation while maintaining a path toward enterprise-grade deployment and supportability.
- Contribute to evaluation frameworks and model quality metrics to ensure solutions meet business, technical, and operational requirements.
- Coach and guide junior engineers where needed, and contribute to engineering standards, reusable components, and delivery best practices.
Required Qualifications
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related field.
- Minimum 7 years of relevant experience, with strong hands-on expertise in AI/ML engineering and production-grade AI solutions.
- Proven experience in software engineering and AI/ML solution delivery, with demonstrated ability to take solutions from prototype to production.
- Strong hands-on programming expertise in Python.
- Practical experience with NumPy, Pandas, and SQL for data handling and transformation.
- Strong understanding of version control and collaborative engineering practices using Git/GitHub.
- Hands-on experience deploying AI/ML services using Flask, FastAPI, and/or MLflow.
- Experience building LLM-powered applications using LangChain and/or LlamaIndex.
- Experience integrating model APIs such as OpenAI, Anthropic, Cohere, or Mistral.
- Knowledge of machine learning concepts sufficient to support model integration, scaling, and operationalization.
- Experience with vector and/or graph databases such as FAISS or Neo4j.
- Strong cross-functional collaboration and communication skills, with the ability to work effectively across product, engineering, DevOps, and compliance teams.
Preferred Technical Skills
- Knowledge of machine learning frameworks such as Scikit-learn, TensorFlow, and PyTorch.
- Understanding of deep learning concepts including transformers, GANs, and autoencoders.
- Practical exposure to NLP concepts such as tokenization, embeddings, attention mechanisms, and language modeling.
- Understanding of computer vision workloads such as image classification, object detection, and segmentation.
- Experience with model fine-tuning and prompt engineering, including few-shot learning and prompt tuning.
- Experience with MLOps practices including experiment tracking, model versioning, output monitoring, and logging.
- Experience setting up production monitoring for drift detection, performance degradation, and automated metric tracking.
- Familiarity with bias detection and mitigation, content filtering and moderation, and explainability/transparency controls.
- Exposure to Azure cloud services and enterprise deployment patterns.
Domain Experience
- Experience in Oil & Gas, Shipping & Trading, energy, or ETRM-related business domains.
- Experience building digital products in regulated or enterprise environments, where solution resilience, security, and compliance are important.