Senior AI/ML Engineer – Applied AI & MLOps

Engagement Type: Contract-to-Hire
Initial Duration: 3–6 Months

Position Overview

We are looking for a skilled Senior AI/ML Engineer to develop and operationalize intelligent applications powered by machine learning and modern Generative AI technologies. This position will focus on taking AI solutions from concept and experimentation through production deployment, with an emphasis on scalable architecture, automation, and reliability.

The ideal professional will bring strong Python engineering capabilities, practical experience implementing Large Language Model (LLM) solutions, and expertise in building Retrieval-Augmented Generation (RAG) applications, autonomous AI workflows, and end-to-end MLOps environments.

Primary Responsibilities

• Architect and implement end-to-end machine learning and Generative AI applications for real-world business use cases.

• Develop retrieval-based AI solutions incorporating document processing, embedding generation, vector indexing, and semantic retrieval.

• Design intelligent AI agents and multi-step workflows capable of handling complex tasks and interactions.

• Implement LLM integration strategies, prompt engineering techniques, and orchestration frameworks.

• Build model deployment and inference services using Python-based technologies and API frameworks.

• Establish automated ML workflows covering model training, validation, versioning, deployment, and ongoing maintenance.

• Implement CI/CD pipelines, containerized environments, and automated processes for reliable AI solution delivery.

• Improve model efficiency by addressing inference speed, resource utilization, scalability, and operational costs.

• Create dependable data preparation and processing pipelines with appropriate quality checks and reproducibility standards.

• Establish evaluation methodologies, performance tracking, monitoring, and safeguards for machine learning and LLM-based applications.

• Work closely with engineering, data, and product stakeholders to translate business requirements into practical AI architectures.

Required Technical Background

• At least 5 years of professional experience in Machine Learning Engineering, Applied AI, or a closely related discipline.

• Advanced programming proficiency in Python, including experience developing production-grade applications.

• Demonstrated ability to transition machine learning models from development environments into production.

• Practical experience implementing Generative AI applications using LLMs, RAG architectures, and agent-based systems.

• Strong understanding of vector embeddings, vector indexing, similarity matching, and semantic retrieval technologies.

• Experience working with frameworks such as LangChain, LlamaIndex, or comparable LLM development ecosystems.

• Familiarity with model deployment and API development technologies such as FastAPI and MLflow.

• Hands-on knowledge of MLOps practices, automated deployment pipelines, Docker/containerization, and cloud-based infrastructure.

• Understanding of model evaluation, observability, performance tuning, and production monitoring for ML and LLM applications.

Additional Qualifications

The following experience would be beneficial:

• Exposure to enterprise AI development platforms and foundation model customization or fine-tuning.

• Familiarity with LLM testing, benchmarking, evaluation, and observability solutions.

• Experience delivering AI/ML solutions in highly controlled or compliance-driven environments.

• Knowledge of large-scale model serving, inference optimization, and efficient compute resource management.