Role overview
The AI/ML Engineer will design and deploy high-impact machine learning systems, working end-to-end from data collection to production-grade solutions. This high-visibility role includes building AI models, engineering advanced pipelines, and implementing agentic AI (autonomous/self-directed AI agents) for forward-thinking, customer-focused solutions.
Responsibilities
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Build, deploy, and optimize robust AI and ML models utilizing prominent tools, frameworks, and cloud services (Azure ML, AWS SageMaker, GCP Vertex AI, or on-premise as needed).
· Build efficient data pipelines for data collection, cleaning, and transformation across structured and unstructured sources.
· Perform feature engineering using tools like Pandas, PySpark, or Databricks to ensure high-quality inputs for ML models.
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Engineer, productionize, and monitor agentic AI workflows, leveraging frameworks such as LangChain, LlamaIndex, OpenAI Agents, Semantic Kernel, or comparable orchestration libraries.
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Develop and integrate APIs or software services for AI solution delivery using languages like Python, Java, or C#.
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Support end-to-end data and model pipeline design, including large language models (LLMs), prompt engineering, and retrieval augmented generation (RAG) architectures.
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Implement best practices in MLOps, CI/CD, containerization (Docker, Kubernetes), and automated testing.
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Collaborate with data scientists, engineers, and customers to translate conceptual models and research into scalable, real-world systems.
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Stay abreast of research, experiment with new agentic and generative AI technologies, and advocate for innovative approaches aligned to business needs.
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Provide mentorship and participate in design/code reviews.
Preferred qualifications
· Experience with Microsoft Azure AI stack (Azure ML, Cognitive Services, Azure OpenAI Service, Synapse).
· Familiarity with Power Platform integrations (Power Automate, Power Apps, Copilot Studio).
· Knowledge of Azure DevOps for CI/CD and secure MLOps workflows.
Qualifications:
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Bachelor’s or Master’s in Computer Science, Engineering, or a related field.
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3-5 years’ experience building and deploying ML systems in cloud or on-premise environments.
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Proficiency in Python (preferred), and familiarity with Java, C++, or C#.
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Hands-on with at least one ML framework (TensorFlow, PyTorch, Keras).
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Experience with leading agentic AI tooling (LangChain, LlamaIndex, Semantic Kernel, OpenAI tools, AutoGen, CrewAI, etc.).
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Practical knowledge of integrating LLMs (OpenAI, Azure OpenAI Service, Hugging Face, etc.) into workflows.
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Familiarity with cloud AI/ML platforms (Azure ML, AWS SageMaker, GCP AI Platform) or strong on-premise deployment knowledge.
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Containerization skills (Docker, Kubernetes) and source control proficiency (Git).
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Understanding of software engineering fundamentals and experience with automated pipelines (MLOps).
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Preferred: Experience with Microsoft AI stack (Azure ML, Cognitive Services, Power Platform) and agent framework integrations is a plus.
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Strong written and verbal communication, a knack for innovation, and a customer-first mindset.