Senior AI ML Engineer

Key Responsibilities
● AI Agent Development: Design, develop, test, and maintain the core logic for AI Agents
within FastAPI services. Orchestrate agent tasks, manage state, interact with platform
data/workflows, and integrate LLM capabilities.
● LLM API Integration & Prompt Engineering: Integrate with external LLM provider
APIs. Design, implement, and rigorously test effective prompts for diverse retail-specific
tasks (generation, Q&A, summarization).
● RAG Implementation: Implement and optimize Retrieval-Augmented Generation
(RAG) patterns using vector databases to provide relevant context to LLM API calls
made by agents.
● FastAPI Microservice Development: Build and maintain the scalable FastAPI
microservices that host AI Agent logic and handle interactions with LLMs and other
platform components in a containerized environment (Docker, Kubernetes).
● Data Processing for AI: Prepare and preprocess data required for effective prompt
context, RAG retrieval, and potentially for future fine-tuning tasks.
● Collaboration & Future Adaptation: Work with cross-functional teams to deliver AI
features. Stay updated on LLM advancements and be prepared to learn and contribute to
potential future in-house LLM fine-tuning and deployment efforts.
Required Skills & Qualifications
● 4-8 years of hands-on experience in software development with a strong focus on AI/ML
application development.
● Demonstrable experience integrating and utilizing external LLM APIs (e.g., OpenAI,
Anthropic, Google) in applications.
● Proven experience with Prompt Engineering techniques.
● Strong Python programming skills.
● Practical experience building and deploying RESTful APIs using FastAPI.
● Experience designing and implementing application logic for AI-driven features or
agents.
● Understanding and practical experience with RAG concepts and vector databases
(Pinecone, FAISS, etc.).
● Solid understanding of core Machine Learning concepts and familiarity with frameworks
like PyTorch, TensorFlow, or Hugging Face (important for understanding models and
future adaptation).
● Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization (Docker,
Kubernetes) for application deployment.
● Solid problem-solving skills and clear communication abilities.
● Experience working effectively in an agile environment.
● Willingness and capacity to learn and adapt towards future work involving deeper LLM
customization and deployment.
● Bachelor's or Master's degree in Computer Science, AI, or a related field.
● Ability to work independently and collaborate effectively in a remote setting.
Preferred Qualifications
● Experience with frameworks like LangChain or LlamaIndex.
● Experience with observability and debugging tools for LLM applications, such as
LangSmith.
● Experience with graph databases (e.g., Neo4j) and query languages (e.g., Cypher).
● Experience with MLOps practices, applicable to both current application monitoring and
future model lifecycle management.
● Experience optimizing API call performance (latency/cost) or model inference.
● Knowledge of AI security considerations and bias mitigation.

Work arrangement
Yes