Hello,
Please review the position below and reply with your details and an updated resume if you are interested.
Location: Alpharetta, GA / NYC, NY - Need Only Locals
Duration: Long Term
Experience: 9+ Years
Work Authorization: No GC
We are seeking an experienced AI/ML Engineer with a strong background in machine learning model development and deployment. The ideal candidate will have hands-on experience building scalable ML solutions and integrating them into production environments.
Design, develop, train, and deploy machine learning models
Integrate ML models with applications using REST APIs
Strong programming experience in Python
Hands-on knowledge of TensorFlow and/or PyTorch
Solid understanding of machine learning algorithms and concepts
Experience taking models from development to production
Experience with Natural Language Processing (NLP) and Deep Learning
Exposure to data preprocessing and analysis tools such as Pandas and NumPy
Familiarity with cloud-based ML platforms:
AWS SageMaker
Azure AI / Azure ML
Basic experience with cloud AI services
Experience working with large datasets
Knowledge of MLOps practices
Ability to collaborate with cross-functional teams
Max | KLNtek
Email: m...@klntek.com
324 E Foothill Blvd, Ste 206, 91006 Arcadia, California
AIML Engineer
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11+ years of experience designing and deploying enterprise-grade AI solutions across healthcare and financial domains, with specialization in Agentic AI systems (LangChain, AutoGen, LangGraph) and advanced LLM fine-tuning (LoRA, PEFT) with RAG.
Proven expertise in Python, TensorFlow, PyTorch, Hugging Face, and MLOps pipelines (MLflow, Kubeflow), alongside NLP, data engineering (BigQuery, Dataflow, Cosmos DB), and secure, scalable deployments via Kubernetes and AKS.
Delivered GenAI-powered clinical decision support systems on Vertex AI for automated EHR summarization, disease prediction, and HIPAA-compliant deployment in high-regulation environments (HIPAA, PCI-DSS, GDPR).
Strong track record mentoring cross-functional teams on AI model development, MLOps best practices, and domain-specific AI implementations in enterprise settings.
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