AI/ML Engineer - Direct Client (Remote)

Dice is the leading career destination for tech experts at every stage of their careers. Our client, SRS Consulting Inc, is seeking the following. Apply via Dice today! Job Title: AI/ML Engineer Location: Remote JOB Duration: 6 Months Duties/Responsibilities: Design and implement MCP servers that expose internal data/services to LLMs Build secure, structured endpoints for model context access Integrate MCP services with model inference APIs Implement and operate a vector search engine Deploy models into production (cloud, on-premise or hybrid) and integrate with upstream/downstream systems (EHR modules, APIs, micro-services, dashboards) Monitor model performance in live settings (accuracy, drift, bias, fairness, reproducibility), and iterate on models to maintain or improve reliability and relevance Build/maintain machine learning pipelines and work with the data platform team to connect AI workloads to core datasets Ensure security, permissions and monitoring of AI systems Implement cost monitoring and usage tracking for AI workloads across internal teams Partner with cross-functional stakeholders (data scientists, data engineers, SDEs) to deploy these capabilities Stay informed about emerging AI/ML techniques, tools and best practices (including AI ethics, bias mitigation, interpretability), and proactively bring forward improvements or innovation Contribute to a culture of continuous improvement, knowledge-sharing and mentoring of junior team members Required Skills: Proficiency in Python (or analogous language) and strong familiarity with ML frameworks/libraries (ex: TensorFlow, PyTorch, scikit-learn) This job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the employee for this job. Duties, responsibilities and activities may change at any time with or without notice. Job Description Experience building APIs, services or microservices Knowledge of vector databases or search systems Experience with LLM application patterns: RAG, embeddings, prompt orchestration and tool calling. Experience with basic MLOps practices : model deployment, monitoring, pipeline automation, CI/CD Demonstrated ability to deploy models into production or near-production environments (cloud environments like AWS, Azure, Google Cloud Platform or containerised/micro-services Infrastructure). Google Cloud Platform Experience Is Strongly Preferred A collaborative mindset, dependable execution, drive to reflect and improve, and humility to ask questions and learn. Education & Experience Bachelor's degree (or equivalent) in Computer Science, Data Science, Statistics, Engineering or a related field 5+ years of platform/infrastructure engineering experience, with demonstrable recent work on LLM-based systems Preferred: Experience in healthcare, behavioral health, EHR systems or regulated industries Familiarity with MLOps practices: CI/CD for models, model monitoring, drift detection, model governance. Experience with NLP (clinical text) or computer vision (imaging) tasks Familiarity with cloud-native services for ML (e.g., AWS SageMaker, Azure ML, Google Cloud Platform AI Platform) and related infrastructure (Docker, Kubernetes) Awareness of AI ethics, bias/fairness issues, model interpretability techniques Apply tot his job