About the Role

The company is seeking an AI/ML Engineer specializing in Generative AI and MLOps to join a customer-facing consulting team. This role focuses on developing, deploying, and supporting production-grade machine learning and generative AI solutions for enterprise clients. You will work across the full AI/ML lifecycle, from data preparation and model development to deployment, monitoring, optimization, and production support. The ideal candidate combines strong Python and machine learning fundamentals with hands-on experience in LLM applications, MLOps, Databricks, and cloud-based AI platforms.

Key Responsibilities

Design, develop, and deploy production-grade machine learning and generative AI applications. Build LLM-based solutions using retrieval-augmented generation (RAG), embeddings, prompt engineering, vector search, and natural language processing techniques. Develop and maintain MLOps pipelines for experimentation, model versioning, testing, deployment, monitoring, and retraining. Manage the model lifecycle using MLflow, including experiment tracking, model registry, evaluation, and production promotion. Deploy and manage models using Databricks Model Serving or equivalent cloud-native platforms. Build and optimize AI workflows using Hugging Face models and libraries, including Transformers where appropriate. Integrate foundation models and APIs from OpenAI, Azure OpenAI, Anthropic, and open-source LLM ecosystems. Develop retrieval pipelines using Databricks Vector Search or other vector database technologies. Build scalable data and feature pipelines using Apache Spark, PySpark, Delta Lake, and Databricks. Containerize machine learning and generative AI workloads using Docker and support automated deployments through CI/CD pipelines. Use orchestration frameworks and scheduling tools to maintain reliable production AI workflows. Monitor model quality, latency, drift, reliability, and overall production performance. Collaborate with client engineering, data, and business teams to translate requirements into deployable AI solutions. Troubleshoot production model, data, API, and pipeline issues across distributed environments. Required Qualifications 3–4+ years of professional experience in machine learning engineering, AI engineering, data science, or a related field. Strong programming skills in Python. Hands-on experience developing and deploying machine learning models in production environments. Practical experience with generative AI, LLMs, RAG, embeddings, and NLP applications. Strong understanding of the MLOps lifecycle, including experimentation, versioning, testing, deployment, monitoring, and retraining. Experience with MLflow or comparable machine learning lifecycle management tools. Experience with Hugging Face, Transformers, or similar model development ecosystems. Experience with Apache Spark, PySpark, and large-scale data processing. Hands-on experience with Docker and production deployment workflows. Experience with REST APIs and integrating models into applications or services. Strong communication skills and the ability to work directly with client stakeholders.

Preferred Qualifications

Hands-on experience with Databricks Machine Learning, MLflow, Model Serving, Vector Search, Feature Engineering, Feature Store, and Mosaic AI. Experience with LangChain, LlamaIndex, Semantic Kernel, or similar generative AI application frameworks. Experience with vector databases and search platforms such as Pinecone, Weaviate, Milvus, FAISS, OpenSearch, or pgvector. Experience with cloud AI/ML services such as AWS SageMaker, Azure Machine Learning, Azure OpenAI, or Google Vertex AI. Experience designing CI/CD pipelines for machine learning and generative AI workloads using GitHub Actions, Azure DevOps, Jenkins, or similar tools. Experience with model and LLM evaluation, observability, guardrails, prompt and version management, and production monitoring. Familiarity with orchestration tools such as Airflow, Databricks Workflows, Prefect, or similar platforms. Familiarity with distributed training, GPU workloads, model optimization, quantization, or fine-tuning techniques. Experience in consulting, customer-facing roles, or technical implementation environments. Location & Work Arrangement United States – Remote. Employment Type & Compensation Contract position (W-2 only; C2C is not available). Hourly rate: $55–$60. Work Authorization Candidates must be authorized to work in the United States without current or future employer sponsorship, including H-1B sponsorship or transfer support.