Overview
On Site
Contract - W2
Contract - 9 day((s))
Skills
LLMs
RAG
fine-tuning
prompt engineering
and vector databases.
Job Details
Job Role : AI/ML Engineer
Key Responsibilities
Key Responsibilities
- : Define and lead AI/ML strategy, architecting end-to-end solutions for complex problems, ensuring scalability and efficiency.
- Model Development: Design, implement, train, and optimize advanced ML/AI models (Deep Learning, NLP, GenAI/LLMs, Computer Vision).
- : Deploy models into production, build robust pipelines, manage lifecycle, monitor performance, and integrate with existing systems.
- : Collaborate with data scientists, engineers, product managers, and stakeholders to translate business needs into technical AI solutions.
- : Guide junior engineers, share expertise, research emerging AI trends, and champion best practices.
Essential Skills & Qualifications
- : Expert Python, proficiency in ML/DL frameworks (TensorFlow, PyTorch, Hugging Face).
- : Strong experience with major cloud providers (AWS, Azure, Google Cloud Platform) and containerization (Docker, Kubernetes).
- : Deep understanding of algorithms, feature engineering, model evaluation, and optimization.
- : Proven experience with LLMs, RAG, fine-tuning, prompt engineering, and vector databases.
- : Data pipelines (ETL/ELT), MLOps tools, big data tech (Spark), and scalable AI architectures.
- : 10+ years in AI/ML, proven track record of delivering production-grade systems, leading projects, and driving technical vision.
Key Technologies (Examples)
- Languages: Python, PySpark
- Frameworks: PyTorch, TensorFlow, Hugging Face, LangChain
- Cloud: AWS, Azure, Google Cloud Platform (Azure DevOps, Databricks a plus)
- Tools: Docker, Kubernetes, Jenkins, MLflow, Vector DBs (Pinecone)
- Concepts: GenAI, LLMs, RAG, MLOps, Scalable Systems, Distributed Computing.
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