Locations:
- New York, NY – 2 Positions
- Alpharetta, GA – 2 Positions
Role Overview
We are seeking a highly motivated AI/ML Engineer to design, develop, deploy, and optimize machine learning and Generative AI solutions. This role blends core ML engineering, Python backend development, and LLM-based systems, including prompt engineering and Retrieval-Augmented Generation (RAG).
The ideal candidate will work closely with product, data, and engineering teams to deliver scalable, production-ready AI solutions that drive real business impact.
Key Responsibilities
1. Machine Learning Model Development & Deployment
- Design, develop, train, and evaluate machine learning models using supervised, unsupervised, and reinforcement learning techniques.
- Perform data cleaning, preprocessing, feature engineering, and exploratory data analysis (EDA) on structured and unstructured datasets.
- Select appropriate algorithms and evaluation metrics based on business use cases.
- Optimize model performance through hyperparameter tuning and experimentation.
- Deploy models into production environments with proper monitoring, versioning, and rollback strategies.
- Implement CI/CD pipelines for ML workflows and model lifecycle management.
2. Prompt Engineering & LLM Orchestration
- Design, test, and optimize prompts for Large Language Models (LLMs) such as GPT-4, Claude, and Llama 3.
- Apply advanced prompting techniques including:
- Chain-of-Thought (CoT)
- Few-Shot and Zero-Shot prompting
- ReAct and tool-augmented reasoning patterns
- Build and maintain Retrieval-Augmented Generation (RAG) pipelines to ensure factual, grounded, and reliable responses.
- Integrate LLMs with proprietary datasets using vector search and embeddings.
- Evaluate LLM output quality and continuously reduce hallucinations and bias.
3. Python Engineering & API Development
- Write clean, scalable, and maintainable Python (3.x) code following best practices.
- Develop RESTful APIs and microservices to expose AI/ML capabilities to internal and external applications.
- Implement asynchronous programming and efficient data pipelines.
- Utilize ML and data science libraries including:
- NumPy, Pandas
- Scikit-learn
- PyTorch and/or TensorFlow
- Collaborate with frontend and backend teams to integrate AI features into core products.
4. Data & Infrastructure Collaboration
- Work with vector databases such as Pinecone, Milvus, Weaviate, or ChromaDB.
- Collaborate with DevOps teams on Dockerized deployments and Kubernetes-based orchestration.
- Ensure AI solutions meet performance, security, and scalability requirements.
- Document architectures, workflows, and design decisions.
Required Skills & Qualifications
Core Technical Skills
- Python: Strong proficiency in Python 3.x, including async programming.
- Machine Learning Fundamentals:
- Statistics, probability, and linear algebra
- Algorithms such as Random Forests, XGBoost, Gradient Boosting, and Neural Networks
- Prompt Engineering: Hands-on experience crafting prompts for reliable and explainable LLM outputs.
- LLM Frameworks: Experience with LangChain, LlamaIndex, or Haystack.
- Databases: Understanding of vector databases and embedding-based retrieval systems.
Soft Skills
- Strong analytical and problem-solving mindset.
- Ability to communicate complex AI/ML concepts to non-technical stakeholders.
- Self-starter with a passion for continuous learning in the rapidly evolving GenAI space.
- Strong collaboration skills across cross-functional teams.
Experience Level Expectations
Entry Level (1–5 Years)
Mandatory:
- Python programming
- Basic TensorFlow or PyTorch
- Understanding of ML algorithms and model evaluation
Preferred:
- Data preprocessing using Pandas and NumPy
- Exposure to cloud-based AI/ML services
Mid Level (5–12 Years)
Mandatory:
- End-to-end model development and deployment
- REST API development for ML models
- Production ML experience
Preferred:
- NLP and deep learning experience
- Experience with cloud ML platforms such as AWS SageMaker or Azure AI
Expert Level (12+ Years)
Mandatory:
- Design and implementation of scalable, enterprise-grade ML systems
- Advanced statistical modeling and optimization techniques
Preferred:
- Leadership in AI architecture and strategy
- Deep experience with Transformers, LLMs, and GenAI frameworks
Preferred Qualifications (All Levels)
- Experience with REST or GraphQL API integrations
- Docker and Kubernetes exposure
- Fine-tuning or adapting open-source models (Hugging Face ecosystem)
- GitHub repository or portfolio showcasing AI/ML projects, prompt libraries, or GenAI solutions
Never miss an opportunity! Create an alert based on the job you applied for.
