Senior AI Solutions Engineer

Job Description

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We are looking for a dynamic AI Solutions Engineer to join our expanding AI team. This role is ideal for a motivated engineer with strong analytical skills and a keen interest in cutting-edge AI technologies. You will have the opportunity to apply modern machine learning (ML) and Generative AI (GenAI) techniques—such as Large Language Models (LLMs), OpenAI/GPT APIs, Google Gemini, and cloud-native ML tools—to solve real-world business problems.

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As an AI Solutions Engineer, you will work closely with cross-functional teams, leveraging your technical expertise and customer-focused insights to design, prototype, and implement AI-driven solutions. Your work will play a crucial role in enhancing enterprise decision-making and automation capabilities. This position offers a unique chance to make a significant impact by integrating advanced AI technologies into business processes.

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AI Solution Development:

• Translate business requirements into comprehensive AI-driven solutions.
• Integrate software applications, data platforms, and cloud infrastructure.

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LLM-Based Application Development:

• Design and implement applications using advanced Large Language Models (LLMs).
• Focus on use cases such as conversational AI, semantic search, Retrieval-Augmented Generation (RAG), and multi-agent systems.

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Proof-of-Concept Creation:

• Develop clear and demonstrative AI prototypes.
• Collaborate with engineering teams to transition successful prototypes into production environments.

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Data Pipelines:

• Design and manage efficient data pipelines to support AI solutions.

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Tooling:

• Utilize and develop tools to streamline AI solution development and deployment.
• Ensure tools are effectively integrated into existing workflows and processes.

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Qualifications:

• 5+ years of experience in data science, AI, or ML with academic or open-source portfolios.
• Proficient in Python, and experienced in using Jupyter Notebooks, Pandas, and data visualization libraries.
• Familiarity with cloud platforms (GCP, AWS, Azure) and data warehouses (e.g., BigQuery, Snowflake).
• Exposure to OpenAI APIs, Hugging Face, or Google Vertex AI is highly desirable.
• Understanding of key AI/ML concepts such as supervised learning, embeddings, fine-tuning, model evaluation metrics, and prompt engineering.

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Preferred Qualifications:

• Master’s in Data Science, Computer Science, AI/ML, Statistics, or a related technical field.
• Hands-on experience with LLMs, embedding models, or semantic search pipelines.
• Proficiency with LangChain, LlamaIndex, LLMOps, and RAG (Retrieval-Augmented Generation) architectures.
• Exposure to model monitoring, data drift detection, or CI/CD in ML pipelines.
• Experience with APIs, microservices, and Python back-end frameworks (FastAPI, Flask).
• Knowledge of responsible AI principles including fairness, interpretability, and data privacy.