AI Engineer

AI Engineer

Research and prototype advanced RAG techniques (e.g., chunking strategies, retrieval optimization, knowledge graph integration).

Explore and implement agentic AI patterns (e.g., supervisor vs plan-execute workflows).

Investigate and develop methods for reducing memory footprint and improving LLM response quality.

Design and experiment with LLM fine-tuning strategies for domain-specific applications.

Collaborate on data preprocessing pipelines, including SQL-based processing and advanced text preprocessing techniques.

Leverage Lang Chain ecosystem tools:
Lang Chain, Lang Graph, Lang Smith for building and evaluating AI workflows.

Experiment with LLMs such as GPT-4+, Gemini, LLaMA, and others for various use cases.

Apply design patterns in AI and backend development for scalability and maintainability.

Contribute to ML Ops and LLM Ops best practices to streamline research-to-production pipelines.

Must-Have Skills & Experience

Programming:
Deep expertise in Python, asynchronous programming, and software design patterns.

Data Processing:
Strong SQL skills, data preprocessing, and transformation techniques.

Lang Chain Ecosystem:
Hands-on experience with Lang Chain, Lang Graph, and Lang Smith.

LLM

Experience:

Familiarity with leading models like GPT-4+, Gemini, LLaMA.

AI Expertise:

o Agentic AI patterns.

o Advanced RAG methods.

o LLM fine-tuning approaches.

o ML Ops & LLM Ops workflows.

Nice-to-Have Skills

Cloud Platforms: AWS or GCP experience.

Dev Ops: CI/CD and containerized environment familiarity