AI/ML Engineer - GenAI & MLOps

Job Description

AI/ML Engineer – GenAI & MLOps

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Location: United States – Remote

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Employment Type: Contract (W-2 only; no C2C)

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Rate: $55–$60/hour

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Experience: 3–4+ years

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Work Authorization: Must be authorized to work in the United States without current or future employer sponsorship, including H-1B sponsorship or transfer support.

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About the Role

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We are seeking a hands-on AI/ML Engineer with strong Generative AI and MLOps experience to join a customer-facing consulting team. This role is focused on building, deploying, and supporting production-grade machine learning and GenAI solutions for enterprise clients.

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You will work across the full AI/ML lifecycle, from data preparation and model development through deployment, monitoring, optimization, and production support. The ideal candidate combines strong Python and machine learning fundamentals with practical experience in LLM applications, MLOps, Databricks, and cloud-based AI platforms.

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Key Responsibilities

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• Design, develop, and deploy production-grade machine learning and Generative AI applications .
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• Build LLM-based solutions using RAG, embeddings, prompt engineering, vector search, and NLP techniques .
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• Develop and maintain MLOps pipelines for experimentation, model versioning, testing, deployment, monitoring, and retraining.
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• Implement model lifecycle management using MLflow , including experiment tracking, model registry, model evaluation, and production promotion.
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• Deploy and manage models using Databricks Model Serving or equivalent cloud-native serving platforms.
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• Build and optimize AI workflows using Hugging Face models and libraries , including Transformers where appropriate.
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• Integrate foundation models and APIs from platforms such as OpenAI, Azure OpenAI, Anthropic, or open-source LLM ecosystems .
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• Develop retrieval pipelines using Databricks Vector Search or other vector database technologies.
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• Design scalable data and feature pipelines using Apache Spark, PySpark, Delta Lake, and Databricks .
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• Containerize ML and GenAI workloads using Docker and support automated deployments through CI/CD pipelines.
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• Work with orchestration frameworks and scheduling tools to support reliable production AI pipelines.
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• Monitor model quality, latency, drift, reliability, and production performance.
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• Partner directly with client engineering, data, and business teams to translate requirements into deployable AI solutions.
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• Troubleshoot production model, data, API, and pipeline issues across distributed environments.
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Required Qualifications

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• 3–4+ years of professional experience in Machine Learning Engineering, AI Engineering, Data Science, or a related field .
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• Strong programming experience with Python .
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• Hands-on experience developing and deploying machine learning models in production environments.
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• Practical experience with Generative AI, LLMs, RAG, embeddings, and NLP applications .
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• Strong understanding of the MLOps lifecycle , including experimentation, versioning, testing, deployment, monitoring, and retraining.
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• Experience with MLflow or comparable ML lifecycle management tools.
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• Experience with Hugging Face , Transformers, or similar model-development ecosystems.
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• Experience with Apache Spark / PySpark and large-scale data processing.
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• Hands-on experience with Docker and production deployment workflows.
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• Experience with REST APIs and integrating models into applications or services.
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• Strong communication skills with the ability to work directly with client stakeholders.
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Preferred Qualifications

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• Hands-on experience with Databricks Machine Learning, MLflow, Model Serving, Vector Search, Feature Engineering/Feature Store, and Mosaic AI capabilities .
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• Experience with LangChain, LlamaIndex, Semantic Kernel , or similar GenAI application frameworks.
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• Experience with vector databases or search platforms such as Pinecone, Weaviate, Milvus, FAISS, OpenSearch, or pgvector .
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• Experience with cloud AI/ML services across AWS SageMaker, Azure Machine Learning/Azure OpenAI, or Google Vertex AI .
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• Experience designing CI/CD pipelines for machine learning and GenAI workloads using GitHub Actions, Azure DevOps, Jenkins, or similar tools.
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• Experience with model and LLM evaluation, observability, guardrails, prompt/version management, and production monitoring.
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• Experience with orchestration tools such as Airflow, Databricks Workflows, Prefect, or similar platforms .
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• Familiarity with distributed training, GPU workloads, model optimization, quantization, or fine-tuning techniques.
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• Experience working in a consulting, customer-facing, or implementation environment .
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