The Senior AI Engineer will design, develop, and deploy advanced AI solutions leveraging large language models (LLMs) and modern frameworks. This role requires expertise in Python, OpenAI, Gemini, GPT-4.0, and emerging AI libraries such as PydanticAI, combined with strong backend development skills using FastAPI, FastMCP, and database technologies like PostgreSQL.
Major Responsibilities
- Participate in creating solutions for functional and non-functional requirements.
- Collaborate with the Scrum team in an agile development environment.
- Contribute to continuous improvement of SDLC processes and methodologies.
- Develop solutions with scalability and flexibility in mind.
- Ensure development delivery meets expectations as defined in a User Story.
- Attend and actively participate in all Sprint meetings.
- Identify roadblocks and escalate to Tech Lead or Scrum Master.
- Assist operations teams with root cause analysis and incident resolution.
AI Model Integration & Development:
- Build and integrate LLM-based solutions using OpenAI, Gemini, and GPT-4.0.
- Implement prompt engineering and Retrieval-Augmented Generation (RAG).
Backend Engineering:
- Develop APIs and microservices using FastAPI and FastMCP.
- Ensure high-performance data flow between AI models and application layers.
Data Management:
- Design and optimize relational schemas in PostgreSQL.
- Implement secure data pipelines for training and inference.
Cloud Deployment:
- Deploy AI workloads on Google Cloud Platform (GCP).
- Optimize infrastructure for scalability and cost efficiency.
Required Qualifications:
- 5+ years of AI/ML engineering experience.
- Hands-on experience with OpenAI, Gemini, GPT-3.5 and GPT-4.
- Strong proficiency in Python and YAML.
- Experience with FastAPI and modern AI frameworks.
Preferred Qualifications:
- Experience with LangChain or orchestration frameworks.
- Knowledge of prompt engineering and AI safety practices.
- Experience with Docker/Kubernetes.
Competencies and Best Practices for High Performers
Software Engineering:
- Applies coding skills to defined capabilities or tasks with minimal guidance.
- Demonstrates an “automation first” mindset to improve quality and efficiency.
- Understands cost, complexity, and capability tradeoffs in system architectures.
- Maintains awareness of upstream and downstream system dependencies.
- Implements monitoring and diagnostic frameworks for delivered features.
- Reuses or develops reusable code, algorithms, and data structures.
- Follows industry trends in technologies, tools, and components.
- Participates in Agile and CI/CD practices.
- Ensures solutions meet security, scalability, performance, and manageability standards.
Customer Centric / Design Thinking:
- Advocates for user needs and contributes to improving user experience.
- Understands business impact and how design decisions affect outcomes.
- Participates in development of prototypes such as mockups, models, and simulations.
Technology Acumen:
- Maintains knowledge of technologies used by the engineering team.
- Continuously expands technical knowledge when necessary.
Business Acumen:
- Contributes to identifying and prioritizing solutions.
- Understands regulatory environments and digital experience channels.
- Designs solutions within specific business channels.
Technology Security Standards:
- Maintains knowledge of current security controls and standards.
Analytical Skills:
- Gathers relevant facts and data to support decision making.
- Uses existing data sources to analyze problems.
- Responds effectively to provided information.
Problem Solving:
- Identifies root causes by eliminating variables.
- Evaluates possible solutions with their pros and cons.
- Uses data-driven approaches to solve problems.
Quality Management:
- Adheres to quality control guidelines and best practices.
- Identifies and reports issues affecting solution quality.
- Recommends improvements for better quality outcomes.
