Machine Learning Engineer SFO, CA (Hybrid)

Machine Learning Engineer SFO, CA (Hybrid)

Duration: 1+ year

Job Description:

NOTE: Must be good in communication and technical skills + Minimum of 8 years of work experience needed.

Role Overview

We are seeking a skilled Machine Learning Engineer to design, develop, and deploy advanced AI/ML models, with a focus on Generative AI, RAG architectures, and large-scale machine learning applications. You will work on end-to-end ML pipelines, integrating state-of-the-art tools like OpenAI, Anthropic Claude, and vector databases to deliver high-quality solutions for real-world business challenges.

Key Responsibilities

1. Machine Learning, Generative AI & RAG Development:
   Build and fine-tune large language models (LLMs) using frameworks such as OpenAI GPT or Anthropic Claude.
   
   Design and implement RAG pipelines for scalable, real-time applications leveraging vector databases like Pinecone, Weaviate, Opensearch.
   
   Develop prompt engineering strategies to optimize model outputs for specific use cases.
   
   Design and deploy scalable ML models that integrate with existing systems.
2. End-to-End ML Pipeline:
   Architect, train, and deploy machine learning pipelines for NLP and multimodal AI solutions.
   
   Conduct data preprocessing, feature engineering, and exploratory data analysis for training datasets.
   
   Optimize embeddings for semantic search and document retrieval tasks.
3. Model Deployment & Optimization:
   Deploy ML models in production environments using cloud platforms like AWS SageMaker, ECS or equivalent tools.
   
   Ensure scalability, reliability, and low latency in production systems while monitoring model performance.
   
   Implement CI/CD pipelines for ML models using Docker, Kubernetes, MLflow.
   
   Ensure APIs and ML services handle high traffic with minimal latency.
4. Security & Compliance:
   Ensure ML APIs follow best practices for authentication, authorization, and data privacy.
5. Collaboration & Integration:
   Work closely with cross-functional teams including data scientists, software engineers, and product managers to align ML solutions with business objectives.
   
   Work with data engineers to design feature stores and streaming pipelines.
   
   Integrate ML outputs into enterprise systems while ensuring seamless user experiences.
6. Research & Innovation:
   Stay updated on advancements in generative AI, LLMs, embeddings, and RAG technologies to enhance existing systems.
   
   Experiment with new algorithms and frameworks to drive innovation in AI-powered applications.

1. Technical Expertise:
   Minimum of 8 years of work experience with at least 4 years in Python; familiarity with frameworks like PyTorch, TensorFlow, and libraries like Hugging Face Transformers.
   
   Hands-on experience with LLMs (e.g., OpenAI GPT models, Anthropic Claude) and fine-tuning techniques.
   
   Strong understanding of RAG architectures and vector database integration (e.g., Opensearch, Pinecone, Weaviate).
2. API Development: FastAPI, Flask, Django
3. Containerization: Docker, AWS ECS, Kubernetes
4. Cloud & Data Tools:
   Experience with cloud platforms such as AWS (SageMaker preferred), GCP Vertex AI, or Azure ML for deploying ML models.
   
   Familiarity with SQL or NoSQL databases for data extraction and preprocessing tasks.
5. Problem-Solving Skills:
   Ability to design scalable solutions for complex problems involving unstructured data and large datasets.
   
   Strong analytical skills with a focus on optimizing ML workflows for performance and efficiency.
6. Soft Skills:
   Excellent communication skills to collaborate effectively with technical and non-technical stakeholders.
   
   A passion for learning and staying ahead in the rapidly evolving field of artificial intelligence.

1. Experience building conversational AI systems or chatbots using generative AI technologies.
2. Experience with building REST API using frameworks such as FastAPI.
3. Experience with SQL and NoSQL database/store (Postgres, DynamoDB, Opensearch etc.)
4. Knowledge of NLP techniques such as sentiment analysis, topic modeling, or summarization tasks.
5. Familiarity with serverless architectures (e.g., AWS Lambda) or ECS for scalable ML deployment.
6. Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, or related fields.