Artificial Intelligence Engineer

As a AI / ML Engineer play an important role in advancing our primary AI platform.

Expectations:

Lead projects focused on integrating and optimizing large language models (LLMs), embedding models, and semantic retrieval systems within our products.

Design, build, and deploy sophisticated multimodal retrieval and agentic systems that deliver powerful insights from unstructured and structured financial data sources.

Drive the next generation of agentic capabilities within our platform, with a focus on composability, advanced function calling, and modular AI orchestration techniques.

Continuously refine our retrieval methodologies and AI outputs by actively leveraging customer feedback, ensuring high accuracy and relevance.

Explore innovative approaches and rapidly prototype new AI-driven features, directly impacting our client’s workflows.

Collaborate closely with engineering, product, and customer-facing teams, communicating ideas clearly and effectively to achieve business outcomes.

Desired Qualifications

2+ years of experience deploying machine learning models in production.

Demonstrated experience integrating closed-source LLM APIs and fine-tuning open-source models

Proven expertise with embedding models and semantic retrieval systems such as Pinecone, Weaviate, FAISS, Chroma, PGVector, etc.

Experience developing multimodal retrieval solutions that integrate diverse data types, including textual, numerical, and structured data.

Strong Python proficiency, including practical knowledge of frameworks like Hugging Face, PyTorch, TensorFlow, or LangChain.

Passion for staying current with cutting-edge AI research, tools, and industry trends.

Ability to thrive in ambiguous environments, independently scoping and executing projects end-to-end.

Excellent communication skills, fostering effective collaboration across technical and business teams.

Additional Qualifications

Prior experience building or optimizing AI agentic systems leveraging function-calling, orchestration frameworks, and modular architectures.

Deep familiarity with financial data retrieval challenges and techniques

Demonstrated experience in performance tuning, scalability improvements, and reliability engineering for AI-driven retrieval systems.

Entrepreneurial mindset or previous experience working in a high-growth startup environment.

Knowledge of or interest in free-form generative AI capabilities and their application within complex business scenarios.