AI ML Engineer

Job Title: AI/ML Engineer
Location: Hybrid - San Francisco, CA 94105

Duration: 6 Months (Possible extension/hire)
 

What You’ll Bring

Must-Haves (Top 3 Resume Requirements):

• AI/ML & LLM Expertise: Proven experience working with Large Language Models and machine learning systems.
• Python Proficiency: Strong development skills in Python for AI/ML applications.
• Agentic AI: Hands-on experience building agent-based systems using frameworks like LangGraph or LangChain.

Role Overview

• AI/ML Engineer to build enterprise-grade chatbots and intelligent assistants using state-of-the-art Large Language Model (LLM) technologies. This role focuses on finance data, Retrieval-Augmented Generation (RAG)-based architectures, hallucination mitigation, and agentic AI systems deployed at scale.
• You will work closely with Product, Data, and Platform teams to deliver reliable, explainable, and production-ready AI solutions.

What You’ll Do

Core Responsibilities:

• Build LLM-powered chatbots using OpenAI, RAG, tool calling, and agent frameworks (LangChain, LangGraph).
• Design Agent-to-Agent (A2A) architectures for multi-step reasoning and autonomous workflows.
• Design and optimize retrieval pipelines using vector databases.
• Implement hallucination reduction techniques, including grounding, re-ranking, citations, and confidence scoring.
• Work with finance and enterprise datasets, ensuring data accuracy and governance.
• Deploy and monitor AI systems using cloud-native and MLOps practices.
• Implement CI/CD pipelines for AI workflows and inference services.

Required Skills & Experience:

• 5+ years of relevant experience in AI/ML engineering.
• Proficiency in Python and SQL.
• Experience with LLMs: OpenAI (GPT-4/4.1), Anthropic, Gemini, Llama.
• Strong knowledge of Agentic AI: LangGraph, LangChain, Agent-to-Agent (A2A) patterns.
• Expertise in RAG & Search: embeddings, hybrid search, cross-encoders.
• Experience with Vector Databases.
• Familiarity with Evaluation & Observability tools: LangSmith, MLflow, Weights & Biases.
• Cloud experience: AWS (S3, Lambda, SageMaker, Bedrock).
• Data engineering skills: Snowflake, DBT, structured and unstructured data pipelines.
• Knowledge of LLM evaluation techniques: prompt/version management, offline and online evaluation.

Preferred Technical Applications:

• AI/ML
• Python
• SQL
• Snowflake