AI ML Engineer

Job description 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 b...uilding 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 Show full description