Generative AI Solutions Engineer

• Business Analysis & Strategy:

• 1. Collaborate closely with stakeholders to gather, analyze, and meticulously document requirements for modern AI-driven projects.
  2. Translate complex business needs into clear, actionable functional and technical specifications for AI solutions.
  3. Conduct thorough feasibility studies and comprehensive Return on Investment (ROI) analyses for new AI initiatives, guiding strategic decision-making.
  4. Support change management efforts and drive the successful adoption of AI solutions across various business units.

• Collaboration & Communication:

• 1. Act as a vital liaison, fostering effective communication and teamwork between diverse business units and technical development teams.
  2. Present insights, prototypes, and project results to collaborators in a clear, concise, and actionable manner, facilitating informed decisions.
  3. Continuously monitor and stay on top of emerging AI technologies, industry standard processes, and innovative approaches, especially within the Generative AI landscape.

• AI Solution Design & Implementation:

• 1. Design, deploy, and maintain scalable, enterprise-grade AI architectures and multi-step agent pipelines, demonstrating a strong understanding of end-to-end solution design patterns and standard processes for GenAI applications.
  2. Build, deploy, and maintain FastAPI endpoints specifically for GenAI agents.
  3. Monitor and continuously improve model performance, latency, and accuracy in production environments, ensuring enterprise-grade standards for reliability, traceability, and maintainability.

You’ll win us over by:

Required Experience:

• Overall Experience - 9+ Years
• 2–3 years of hands-on experience in the full lifecycle of AI development and operations, with a strong focus on generative AI solutions and agent implementation, including successful integration into production environments.

Technical Expertise (Hard Skills):

• Generative AI & Machine Learning: In-depth knowledge and hands-on experience with core GenAI components (e.g., Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, embeddings, agent systems) as well as foundational classical machine learning concepts.
• Cloud ML Platforms: Validated experience with leading cloud-based ML services such as Azure ML Studio, Azure OpenAI Service, AWS SageMaker, or AWS Bedrock.
• Containerization & Orchestration: Expertise with containerization technologies (e.g., Docker, Kubernetes, Azure Container Apps, AWS Fargate).
• DevOps & MLOps: Strong understanding and experience with GitHub workflows, CI/CD pipeline management, and robust deployment automation strategies.
• Programming & Frameworks: Proficiency in Python and familiarity with modern ML frameworks and agent frameworks (such as LangGraph and ADK).

Professional Skills (Soft Skills):

• Agile Methodologies: Practical experience with agile development methodologies (e.g., Scrum).
• Problem-Solving: Strong troubleshooting and analytical problem-solving skills, with an ability to solve complex technical challenges effectively.
  • Software Engineering Principles: Excellent software engineering skills, including a commitment to good coding practices, clean architecture, and maintainable code.